Overview

Brought to you by YData

Dataset statistics

 TrainTest
Number of variables8989
Number of observations2688111521
Missing cells00
Missing cells (%)0.0%0.0%
Total size in memory18.5 MiB7.9 MiB
Average record size in memory720.0 B720.0 B

Variable types

 TrainTest
Numeric7573
Categorical1315
Boolean11

Alerts

TrainTest
campaign_id has constant value "1" campaign_id has constant value "1" Constant
direct_mail_flag has constant value "True" direct_mail_flag has constant value "True" Constant
AGE is highly overall correlated with MARITALSTATUSAGE is highly overall correlated with MARITALSTATUSHigh correlation
Age_Newest_TL is highly overall correlated with Tot_Active_TL and 8 other fieldsAge_Newest_TL is highly overall correlated with Tot_Active_TL and 6 other fieldsHigh correlation
Age_Oldest_TL is highly overall correlated with Secured_TL and 6 other fieldsAge_Oldest_TL is highly overall correlated with Secured_TL and 6 other fieldsHigh correlation
CC_Flag is highly overall correlated with CC_utilization and 1 other fieldsCC_Flag is highly overall correlated with CC_enq and 3 other fieldsHigh correlation
CC_TL is highly overall correlated with CC_enq and 2 other fieldsCC_TL is highly overall correlated with CC_enq and 2 other fieldsHigh correlation
CC_enq is highly overall correlated with CC_TL and 12 other fieldsCC_enq is highly overall correlated with CC_Flag and 13 other fieldsHigh correlation
CC_enq_L12m is highly overall correlated with CC_TL and 12 other fieldsCC_enq_L12m is highly overall correlated with CC_Flag and 13 other fieldsHigh correlation
CC_enq_L6m is highly overall correlated with CC_enq and 10 other fieldsCC_enq_L6m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
CC_utilization is highly overall correlated with CC_Flag and 3 other fieldsCC_utilization is highly overall correlated with CC_Flag and 3 other fieldsHigh correlation
Consumer_TL is highly overall correlated with Total_TL_opened_L12M and 2 other fieldsConsumer_TL is highly overall correlated with Total_TL_opened_L12M and 2 other fieldsHigh correlation
Credit_Score is highly overall correlated with enq_L3mCredit_Score is highly overall correlated with enq_L3mHigh correlation
EDUCATION is highly overall correlated with PROSPECTIDEDUCATION is highly overall correlated with PROSPECTIDHigh correlation
GENDER is highly overall correlated with PROSPECTIDGENDER is highly overall correlated with PROSPECTIDHigh correlation
GL_Flag is highly overall correlated with Home_TL and 1 other fieldsGL_Flag is highly overall correlated with Home_TL and 1 other fieldsHigh correlation
Gold_TL is highly overall correlated with Secured_TL and 1 other fieldsGold_TL is highly overall correlated with Secured_TL and 2 other fieldsHigh correlation
HL_Flag is highly overall correlated with PROSPECTIDHL_Flag is highly overall correlated with PROSPECTIDHigh correlation
Home_TL is highly overall correlated with GL_FlagHome_TL is highly overall correlated with GL_FlagHigh correlation
MARITALSTATUS is highly overall correlated with AGE and 1 other fieldsMARITALSTATUS is highly overall correlated with AGE and 1 other fieldsHigh correlation
PL_Flag is highly overall correlated with PL_utilization and 1 other fieldsPL_Flag is highly overall correlated with PL_utilization and 1 other fieldsHigh correlation
PL_TL is highly overall correlated with PL_enq and 2 other fieldsPL_TL is highly overall correlated with PL_enq and 1 other fieldsHigh correlation
PL_enq is highly overall correlated with CC_enq and 13 other fieldsPL_enq is highly overall correlated with CC_enq and 12 other fieldsHigh correlation
PL_enq_L12m is highly overall correlated with CC_enq and 10 other fieldsPL_enq_L12m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
PL_enq_L6m is highly overall correlated with CC_enq and 10 other fieldsPL_enq_L6m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
PL_utilization is highly overall correlated with PL_Flag and 2 other fieldsPL_utilization is highly overall correlated with PL_Flag and 2 other fieldsHigh correlation
PROSPECTID is highly overall correlated with CC_Flag and 11 other fieldsPROSPECTID is highly overall correlated with CC_Flag and 13 other fieldsHigh correlation
Secured_TL is highly overall correlated with Age_Oldest_TL and 3 other fieldsSecured_TL is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
Tot_Active_TL is highly overall correlated with Age_Newest_TL and 7 other fieldsTot_Active_TL is highly overall correlated with Age_Newest_TL and 7 other fieldsHigh correlation
Tot_Closed_TL is highly overall correlated with Age_Oldest_TL and 9 other fieldsTot_Closed_TL is highly overall correlated with Age_Oldest_TL and 9 other fieldsHigh correlation
Tot_Missed_Pmnt is highly overall correlated with Age_Newest_TL and 2 other fieldsTot_Missed_Pmnt is highly overall correlated with Age_Newest_TL and 3 other fieldsHigh correlation
Tot_TL_closed_L12M is highly overall correlated with Tot_Closed_TL and 4 other fieldsTot_TL_closed_L12M is highly overall correlated with Tot_Closed_TL and 4 other fieldsHigh correlation
Tot_TL_closed_L6M is highly overall correlated with Tot_Closed_TL and 3 other fieldsTot_TL_closed_L6M is highly overall correlated with Tot_Closed_TL and 3 other fieldsHigh correlation
Total_TL is highly overall correlated with Age_Oldest_TL and 6 other fieldsTotal_TL is highly overall correlated with Age_Oldest_TL and 7 other fieldsHigh correlation
Total_TL_opened_L12M is highly overall correlated with Age_Newest_TL and 9 other fieldsTotal_TL_opened_L12M is highly overall correlated with Age_Newest_TL and 10 other fieldsHigh correlation
Total_TL_opened_L6M is highly overall correlated with Age_Newest_TL and 5 other fieldsTotal_TL_opened_L6M is highly overall correlated with Age_Newest_TL and 5 other fieldsHigh correlation
Unsecured_TL is highly overall correlated with Consumer_TL and 6 other fieldsUnsecured_TL is highly overall correlated with Consumer_TL and 6 other fieldsHigh correlation
enq_L12m is highly overall correlated with Age_Newest_TL and 12 other fieldsenq_L12m is highly overall correlated with CC_enq and 11 other fieldsHigh correlation
enq_L3m is highly overall correlated with CC_enq and 9 other fieldsenq_L3m is highly overall correlated with CC_enq and 9 other fieldsHigh correlation
enq_L6m is highly overall correlated with CC_enq and 10 other fieldsenq_L6m is highly overall correlated with CC_enq and 10 other fieldsHigh correlation
first_prod_enq2 is highly overall correlated with PROSPECTIDfirst_prod_enq2 is highly overall correlated with PROSPECTIDHigh correlation
last_prod_enq2 is highly overall correlated with PROSPECTIDlast_prod_enq2 is highly overall correlated with PROSPECTIDHigh correlation
max_delinquency_level is highly overall correlated with max_deliq_12mts and 9 other fieldsmax_delinquency_level is highly overall correlated with max_deliq_12mts and 9 other fieldsHigh correlation
max_deliq_12mts is highly overall correlated with max_delinquency_level and 7 other fieldsmax_deliq_12mts is highly overall correlated with max_delinquency_level and 7 other fieldsHigh correlation
max_deliq_6mts is highly overall correlated with max_deliq_12mts and 2 other fieldsmax_deliq_6mts is highly overall correlated with max_deliq_12mts and 2 other fieldsHigh correlation
max_recent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fieldsmax_recent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fieldsHigh correlation
max_unsec_exposure_inPct is highly overall correlated with PL_TL and 4 other fieldsmax_unsec_exposure_inPct is highly overall correlated with PL_utilization and 2 other fieldsHigh correlation
num_dbt is highly overall correlated with num_dbt_6mtsnum_dbt is highly overall correlated with num_dbt_12mts and 1 other fieldsHigh correlation
num_dbt_12mts is highly overall correlated with num_dbt_6mtsnum_dbt_12mts is highly overall correlated with num_dbt and 1 other fieldsHigh correlation
num_dbt_6mts is highly overall correlated with PROSPECTID and 2 other fieldsnum_dbt_6mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_deliq_12mts is highly overall correlated with max_delinquency_level and 9 other fieldsnum_deliq_12mts is highly overall correlated with max_delinquency_level and 9 other fieldsHigh correlation
num_deliq_6_12mts is highly overall correlated with max_delinquency_level and 6 other fieldsnum_deliq_6_12mts is highly overall correlated with max_delinquency_level and 6 other fieldsHigh correlation
num_deliq_6mts is highly overall correlated with max_deliq_12mts and 3 other fieldsnum_deliq_6mts is highly overall correlated with max_deliq_12mts and 3 other fieldsHigh correlation
num_lss is highly overall correlated with num_lss_6mtsnum_lss is highly overall correlated with num_lss_12mts and 1 other fieldsHigh correlation
num_lss_12mts is highly overall correlated with num_lss_6mtsnum_lss_12mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_lss_6mts is highly overall correlated with PROSPECTID and 2 other fieldsnum_lss_6mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_std is highly overall correlated with num_std_12mts and 1 other fieldsnum_std is highly overall correlated with num_std_12mts and 1 other fieldsHigh correlation
num_std_12mts is highly overall correlated with num_std and 1 other fieldsnum_std_12mts is highly overall correlated with num_std and 1 other fieldsHigh correlation
num_std_6mts is highly overall correlated with num_std and 1 other fieldsnum_std_6mts is highly overall correlated with num_std and 1 other fieldsHigh correlation
num_sub is highly overall correlated with num_sub_12mtsnum_sub is highly overall correlated with num_sub_6mtsHigh correlation
num_sub_12mts is highly overall correlated with num_sub and 1 other fieldsnum_sub_12mts is highly overall correlated with num_sub_6mtsHigh correlation
num_sub_6mts is highly overall correlated with num_sub_12mtsnum_sub_6mts is highly overall correlated with PROSPECTID and 2 other fieldsHigh correlation
num_times_30p_dpd is highly overall correlated with max_delinquency_level and 6 other fieldsnum_times_30p_dpd is highly overall correlated with max_delinquency_level and 6 other fieldsHigh correlation
num_times_60p_dpd is highly overall correlated with max_delinquency_level and 5 other fieldsnum_times_60p_dpd is highly overall correlated with max_delinquency_level and 5 other fieldsHigh correlation
num_times_delinquent is highly overall correlated with max_delinquency_level and 10 other fieldsnum_times_delinquent is highly overall correlated with max_delinquency_level and 10 other fieldsHigh correlation
pct_CC_enq_L6m_of_L12m is highly overall correlated with CC_enq and 3 other fieldspct_CC_enq_L6m_of_L12m is highly overall correlated with CC_enq and 3 other fieldsHigh correlation
pct_CC_enq_L6m_of_ever is highly overall correlated with CC_enq and 3 other fieldspct_CC_enq_L6m_of_ever is highly overall correlated with CC_enq and 3 other fieldsHigh correlation
pct_PL_enq_L6m_of_L12m is highly overall correlated with PL_enq and 5 other fieldspct_PL_enq_L6m_of_L12m is highly overall correlated with PL_enq and 5 other fieldsHigh correlation
pct_PL_enq_L6m_of_ever is highly overall correlated with PL_enq and 5 other fieldspct_PL_enq_L6m_of_ever is highly overall correlated with PL_enq and 5 other fieldsHigh correlation
pct_active_tl is highly overall correlated with Age_Oldest_TL and 3 other fieldspct_active_tl is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
pct_closed_tl is highly overall correlated with Age_Oldest_TL and 3 other fieldspct_closed_tl is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
pct_currentBal_all_TL is highly overall correlated with Age_Newest_TLAlert not present in this datasetHigh correlation
pct_of_active_TLs_ever is highly overall correlated with Age_Oldest_TL and 3 other fieldspct_of_active_TLs_ever is highly overall correlated with Age_Oldest_TL and 3 other fieldsHigh correlation
pct_opened_TLs_L6m_of_L12m is highly overall correlated with Age_Newest_TL and 3 other fieldspct_opened_TLs_L6m_of_L12m is highly overall correlated with Age_Newest_TL and 3 other fieldsHigh correlation
pct_tl_closed_L12M is highly overall correlated with Tot_Closed_TL and 3 other fieldspct_tl_closed_L12M is highly overall correlated with Tot_Closed_TL and 3 other fieldsHigh correlation
pct_tl_closed_L6M is highly overall correlated with Tot_TL_closed_L12M and 2 other fieldspct_tl_closed_L6M is highly overall correlated with Tot_TL_closed_L12M and 2 other fieldsHigh correlation
pct_tl_open_L12M is highly overall correlated with Age_Newest_TL and 3 other fieldspct_tl_open_L12M is highly overall correlated with Age_Newest_TL and 3 other fieldsHigh correlation
pct_tl_open_L6M is highly overall correlated with Age_Newest_TL and 4 other fieldspct_tl_open_L6M is highly overall correlated with Age_Newest_TL and 4 other fieldsHigh correlation
recent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fieldsrecent_level_of_deliq is highly overall correlated with max_delinquency_level and 9 other fieldsHigh correlation
response_flag is highly overall correlated with PROSPECTIDresponse_flag is highly overall correlated with PROSPECTIDHigh correlation
time_since_first_deliquency is highly overall correlated with max_delinquency_level and 8 other fieldstime_since_first_deliquency is highly overall correlated with max_delinquency_level and 8 other fieldsHigh correlation
time_since_recent_deliquency is highly overall correlated with max_delinquency_level and 6 other fieldstime_since_recent_deliquency is highly overall correlated with max_delinquency_level and 6 other fieldsHigh correlation
tot_enq is highly overall correlated with CC_enq and 11 other fieldstot_enq is highly overall correlated with CC_enq and 11 other fieldsHigh correlation
response_flag is highly imbalanced (67.9%) response_flag is highly imbalanced (69.8%) Imbalance
num_dbt_6mts is highly imbalanced (99.8%) num_dbt_6mts is highly imbalanced (99.8%) Imbalance
num_lss_6mts is highly imbalanced (99.9%) num_lss_6mts is highly imbalanced (99.8%) Imbalance
GL_Flag is highly imbalanced (68.9%) GL_Flag is highly imbalanced (68.4%) Imbalance
max_deliq_6mts is highly skewed (γ1 = 20.63945) Alert not present in this datasetSkewed
num_sub is highly skewed (γ1 = 22.11940752) Alert not present in this datasetSkewed
num_sub_6mts is highly skewed (γ1 = 47.08338708) Alert not present in this datasetSkewed
num_sub_12mts is highly skewed (γ1 = 32.33341137) num_sub_12mts is highly skewed (γ1 = 36.14461754) Skewed
num_dbt is highly skewed (γ1 = 34.12793416) num_dbt is highly skewed (γ1 = 36.47892137) Skewed
num_dbt_12mts is highly skewed (γ1 = 56.20134414) num_dbt_12mts is highly skewed (γ1 = 48.56599513) Skewed
num_lss is highly skewed (γ1 = 68.3217958) num_lss is highly skewed (γ1 = 42.50113193) Skewed
num_lss_12mts is highly skewed (γ1 = 91.25653726) Alert not present in this datasetSkewed
pct_currentBal_all_TL is highly skewed (γ1 = 140.3252104) Alert not present in this datasetSkewed
max_unsec_exposure_inPct is highly skewed (γ1 = 51.02078163) max_unsec_exposure_inPct is highly skewed (γ1 = 89.67941116) Skewed
PROSPECTID has unique values PROSPECTID has unique values Unique
num_times_delinquent has 18396 (68.4%) zeros num_times_delinquent has 7906 (68.6%) zeros Zeros
max_recent_level_of_deliq has 18396 (68.4%) zeros max_recent_level_of_deliq has 7906 (68.6%) zeros Zeros
num_deliq_6mts has 24047 (89.5%) zeros num_deliq_6mts has 10269 (89.1%) zeros Zeros
num_deliq_12mts has 22082 (82.1%) zeros num_deliq_12mts has 9429 (81.8%) zeros Zeros
num_deliq_6_12mts has 23403 (87.1%) zeros num_deliq_6_12mts has 9974 (86.6%) zeros Zeros
max_deliq_6mts has 17922 (66.7%) zeros max_deliq_6mts has 7633 (66.3%) zeros Zeros
max_deliq_12mts has 16962 (63.1%) zeros max_deliq_12mts has 7201 (62.5%) zeros Zeros
num_times_30p_dpd has 22496 (83.7%) zeros num_times_30p_dpd has 9667 (83.9%) zeros Zeros
num_times_60p_dpd has 24185 (90.0%) zeros num_times_60p_dpd has 10396 (90.2%) zeros Zeros
num_std has 16577 (61.7%) zeros num_std has 7061 (61.3%) zeros Zeros
num_std_6mts has 19529 (72.6%) zeros num_std_6mts has 8393 (72.8%) zeros Zeros
num_std_12mts has 18658 (69.4%) zeros num_std_12mts has 7999 (69.4%) zeros Zeros
num_sub has 26562 (98.8%) zeros num_sub has 11384 (98.8%) zeros Zeros
num_sub_6mts has 26856 (99.9%) zeros Alert not present in this datasetZeros
num_sub_12mts has 26800 (99.7%) zeros num_sub_12mts has 11489 (99.7%) zeros Zeros
num_dbt has 26790 (99.7%) zeros num_dbt has 11489 (99.7%) zeros Zeros
num_dbt_12mts has 26864 (99.9%) zeros num_dbt_12mts has 11512 (99.9%) zeros Zeros
num_lss has 26823 (99.8%) zeros num_lss has 11501 (99.8%) zeros Zeros
num_lss_12mts has 26868 (> 99.9%) zeros Alert not present in this datasetZeros
recent_level_of_deliq has 18396 (68.4%) zeros recent_level_of_deliq has 7906 (68.6%) zeros Zeros
CC_enq has 17558 (65.3%) zeros CC_enq has 7590 (65.9%) zeros Zeros
CC_enq_L6m has 20703 (77.0%) zeros CC_enq_L6m has 8914 (77.4%) zeros Zeros
CC_enq_L12m has 19454 (72.4%) zeros CC_enq_L12m has 8393 (72.8%) zeros Zeros
PL_enq has 13144 (48.9%) zeros PL_enq has 5680 (49.3%) zeros Zeros
PL_enq_L6m has 17559 (65.3%) zeros PL_enq_L6m has 7558 (65.6%) zeros Zeros
PL_enq_L12m has 15334 (57.0%) zeros PL_enq_L12m has 6640 (57.6%) zeros Zeros
enq_L12m has 4643 (17.3%) zeros enq_L12m has 2064 (17.9%) zeros Zeros
enq_L6m has 7885 (29.3%) zeros enq_L6m has 3426 (29.7%) zeros Zeros
enq_L3m has 10756 (40.0%) zeros enq_L3m has 4675 (40.6%) zeros Zeros
pct_of_active_TLs_ever has 4077 (15.2%) zeros pct_of_active_TLs_ever has 1682 (14.6%) zeros Zeros
pct_opened_TLs_L6m_of_L12m has 15421 (57.4%) zeros pct_opened_TLs_L6m_of_L12m has 6654 (57.8%) zeros Zeros
pct_currentBal_all_TL has 5615 (20.9%) zeros pct_currentBal_all_TL has 2362 (20.5%) zeros Zeros
pct_PL_enq_L6m_of_L12m has 20545 (76.4%) zeros pct_PL_enq_L6m_of_L12m has 8785 (76.3%) zeros Zeros
pct_CC_enq_L6m_of_L12m has 23689 (88.1%) zeros pct_CC_enq_L6m_of_L12m has 10141 (88.0%) zeros Zeros
pct_PL_enq_L6m_of_ever has 20545 (76.4%) zeros pct_PL_enq_L6m_of_ever has 8785 (76.3%) zeros Zeros
pct_CC_enq_L6m_of_ever has 23689 (88.1%) zeros pct_CC_enq_L6m_of_ever has 10141 (88.0%) zeros Zeros
max_unsec_exposure_inPct has 275 (1.0%) zeros Alert not present in this datasetZeros
Tot_Closed_TL has 9440 (35.1%) zeros Tot_Closed_TL has 4047 (35.1%) zeros Zeros
Tot_Active_TL has 4077 (15.2%) zeros Tot_Active_TL has 1682 (14.6%) zeros Zeros
Total_TL_opened_L6M has 15421 (57.4%) zeros Total_TL_opened_L6M has 6654 (57.8%) zeros Zeros
Tot_TL_closed_L6M has 19593 (72.9%) zeros Tot_TL_closed_L6M has 8371 (72.7%) zeros Zeros
pct_tl_open_L6M has 15421 (57.4%) zeros pct_tl_open_L6M has 6654 (57.8%) zeros Zeros
pct_tl_closed_L6M has 19593 (72.9%) zeros pct_tl_closed_L6M has 8371 (72.7%) zeros Zeros
pct_active_tl has 4077 (15.2%) zeros pct_active_tl has 1682 (14.6%) zeros Zeros
pct_closed_tl has 9440 (35.1%) zeros pct_closed_tl has 4047 (35.1%) zeros Zeros
Total_TL_opened_L12M has 8956 (33.3%) zeros Total_TL_opened_L12M has 3852 (33.4%) zeros Zeros
Tot_TL_closed_L12M has 16310 (60.7%) zeros Tot_TL_closed_L12M has 6996 (60.7%) zeros Zeros
pct_tl_open_L12M has 8956 (33.3%) zeros pct_tl_open_L12M has 3852 (33.4%) zeros Zeros
pct_tl_closed_L12M has 16310 (60.7%) zeros pct_tl_closed_L12M has 6996 (60.7%) zeros Zeros
Tot_Missed_Pmnt has 17534 (65.2%) zeros Tot_Missed_Pmnt has 7529 (65.4%) zeros Zeros
Auto_TL has 15135 (56.3%) zeros Auto_TL has 6532 (56.7%) zeros Zeros
CC_TL has 21879 (81.4%) zeros CC_TL has 9375 (81.4%) zeros Zeros
Consumer_TL has 14347 (53.4%) zeros Consumer_TL has 6189 (53.7%) zeros Zeros
Gold_TL has 19461 (72.4%) zeros Gold_TL has 8398 (72.9%) zeros Zeros
Home_TL has 25377 (94.4%) zeros Home_TL has 10863 (94.3%) zeros Zeros
PL_TL has 21662 (80.6%) zeros PL_TL has 9343 (81.1%) zeros Zeros
Secured_TL has 7585 (28.2%) zeros Secured_TL has 3270 (28.4%) zeros Zeros
Unsecured_TL has 8454 (31.4%) zeros Unsecured_TL has 3604 (31.3%) zeros Zeros
Other_TL has 15127 (56.3%) zeros Other_TL has 6457 (56.0%) zeros Zeros
Alert not present in this datasetnum_sub_6mts is highly imbalanced (99.6%) Imbalance
Alert not present in this datasetnum_lss_12mts is highly imbalanced (99.8%) Imbalance
Alert not present in this datasetNETMONTHLYINCOME is highly skewed (γ1 = 55.07041449) Skewed
Alert not present in this datasetCC_utilization has 119 (1.0%) zeros Zeros

Reproduction

 TrainTest
Analysis started2025-03-11 06:49:48.3830372025-03-11 06:50:03.774922
Analysis finished2025-03-11 06:50:03.7729362025-03-11 06:50:14.232154
Duration15.39 seconds10.46 seconds
Software versionydata-profiling v0.0.dev0ydata-profiling v0.0.dev0
Download configurationconfig.jsonconfig.json

Variables

PROSPECTID
Real number (ℝ)

 TrainTest
Distinct2688111521
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean288005.49286322.41
 TrainTest
Minimum506
Maximum575916575868
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:14.354156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum506
5-th percentile2796628234
Q1143747144902
median288881286228
Q3430581429301
95-th percentile548031543919
Maximum575916575868
Range575866575862
Interquartile range (IQR)286834284399

Descriptive statistics

 TrainTest
Standard deviation166320.8165516.03
Coefficient of variation (CV)0.577491750.57807569
Kurtosis-1.1947889-1.1922891
Mean288005.49286322.41
Median Absolute Deviation (MAD)143477142032
Skewness-0.00305894240.00068900159
Sum7.7418756 × 1093.2987205 × 109
Variance2.7662607 × 10102.7395555 × 1010
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:14.475098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
324643 1
 
< 0.1%
6446 1
 
< 0.1%
5320 1
 
< 0.1%
375117 1
 
< 0.1%
216942 1
 
< 0.1%
535179 1
 
< 0.1%
85131 1
 
< 0.1%
175428 1
 
< 0.1%
192516 1
 
< 0.1%
181667 1
 
< 0.1%
Other values (26871) 26871
> 99.9%
ValueCountFrequency (%)
468895 1
 
< 0.1%
179182 1
 
< 0.1%
522483 1
 
< 0.1%
283309 1
 
< 0.1%
190263 1
 
< 0.1%
172720 1
 
< 0.1%
313123 1
 
< 0.1%
176976 1
 
< 0.1%
306524 1
 
< 0.1%
412079 1
 
< 0.1%
Other values (11511) 11511
99.9%
ValueCountFrequency (%)
50 1
< 0.1%
60 1
< 0.1%
102 1
< 0.1%
109 1
< 0.1%
139 1
< 0.1%
143 1
< 0.1%
145 1
< 0.1%
148 1
< 0.1%
170 1
< 0.1%
174 1
< 0.1%
ValueCountFrequency (%)
6 1
< 0.1%
67 1
< 0.1%
129 1
< 0.1%
371 1
< 0.1%
383 1
< 0.1%
493 1
< 0.1%
549 1
< 0.1%
584 1
< 0.1%
678 1
< 0.1%
737 1
< 0.1%
ValueCountFrequency (%)
6 1
< 0.1%
67 1
< 0.1%
129 1
< 0.1%
371 1
< 0.1%
383 1
< 0.1%
493 1
< 0.1%
549 1
< 0.1%
584 1
< 0.1%
678 1
< 0.1%
737 1
< 0.1%
ValueCountFrequency (%)
50 1
< 0.1%
60 1
< 0.1%
102 1
< 0.1%
109 1
< 0.1%
139 1
< 0.1%
143 1
< 0.1%
145 1
< 0.1%
148 1
< 0.1%
170 1
< 0.1%
174 1
< 0.1%

campaign_id
Categorical

 TrainTest
Distinct11
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
1
26881 
1
11521 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters11
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
1 26881
100.0%
ValueCountFrequency (%)
1 11521
100.0%

Length

2025-03-10T23:50:14.569735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:14.627940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:14.675476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 26881
100.0%
ValueCountFrequency (%)
1 11521
100.0%

Most occurring characters

ValueCountFrequency (%)
1 26881
100.0%
ValueCountFrequency (%)
1 11521
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 26881
100.0%
ValueCountFrequency (%)
1 11521
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 26881
100.0%
ValueCountFrequency (%)
1 11521
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 26881
100.0%
ValueCountFrequency (%)
1 11521
100.0%

response_flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0.0
25314 
1.0
 
1567
0.0
10901 
1.0
 
620

Length

 TrainTest
Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

 TrainTest
Total characters8064334563
Distinct characters33
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row0.00.0
2nd row0.00.0
3rd row0.00.0
4th row0.00.0
5th row0.00.0

Common Values

ValueCountFrequency (%)
0.0 25314
94.2%
1.0 1567
 
5.8%
ValueCountFrequency (%)
0.0 10901
94.6%
1.0 620
 
5.4%

Length

2025-03-10T23:50:14.737031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:14.796755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:14.847361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 25314
94.2%
1.0 1567
 
5.8%
ValueCountFrequency (%)
0.0 10901
94.6%
1.0 620
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 52195
64.7%
. 26881
33.3%
1 1567
 
1.9%
ValueCountFrequency (%)
0 22422
64.9%
. 11521
33.3%
1 620
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80643
100.0%
ValueCountFrequency (%)
(unknown) 34563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 52195
64.7%
. 26881
33.3%
1 1567
 
1.9%
ValueCountFrequency (%)
0 22422
64.9%
. 11521
33.3%
1 620
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80643
100.0%
ValueCountFrequency (%)
(unknown) 34563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 52195
64.7%
. 26881
33.3%
1 1567
 
1.9%
ValueCountFrequency (%)
0 22422
64.9%
. 11521
33.3%
1 620
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80643
100.0%
ValueCountFrequency (%)
(unknown) 34563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 52195
64.7%
. 26881
33.3%
1 1567
 
1.9%
ValueCountFrequency (%)
0 22422
64.9%
. 11521
33.3%
1 620
 
1.8%
 TrainTest
Distinct11
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size236.3 KiB101.3 KiB
True
26881 
True
11521 
ValueCountFrequency (%)
True 26881
100.0%
ValueCountFrequency (%)
True 11521
100.0%

Train

2025-03-10T23:50:14.908888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:14.957433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

time_since_recent_payment
Real number (ℝ)

 TrainTest
Distinct17441286
Distinct (%)6.5%11.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean213.02727208.99401
 TrainTest
Minimum-1-1
Maximum55406065
Zeros00
Zeros (%)0.0%0.0%
Negative2114922
Negative (%)7.9%8.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:15.046204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q14444
median6666
Q3134129
95-th percentile10471000
Maximum55406065
Range55416066
Interquartile range (IQR)9085

Descriptive statistics

 TrainTest
Standard deviation438.30549429.24792
Coefficient of variation (CV)2.05750882.0538767
Kurtosis26.06684629.803501
Mean213.02727208.99401
Median Absolute Deviation (MAD)2828
Skewness4.45972774.6226164
Sum57263862407820
Variance192111.7184253.78
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:15.162878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 2114
 
7.9%
50 366
 
1.4%
54 357
 
1.3%
43 350
 
1.3%
46 344
 
1.3%
59 336
 
1.2%
49 330
 
1.2%
52 323
 
1.2%
53 311
 
1.2%
63 310
 
1.2%
Other values (1734) 21740
80.9%
ValueCountFrequency (%)
-1 922
 
8.0%
51 152
 
1.3%
49 151
 
1.3%
47 149
 
1.3%
59 147
 
1.3%
50 145
 
1.3%
61 140
 
1.2%
43 140
 
1.2%
46 136
 
1.2%
53 136
 
1.2%
Other values (1276) 9303
80.7%
ValueCountFrequency (%)
-1 2114
7.9%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 6
 
< 0.1%
6 8
 
< 0.1%
7 10
 
< 0.1%
8 6
 
< 0.1%
9 7
 
< 0.1%
10 13
 
< 0.1%
11 12
 
< 0.1%
ValueCountFrequency (%)
-1 922
8.0%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 8
 
0.1%
9 2
 
< 0.1%
10 6
 
0.1%
11 3
 
< 0.1%
12 5
 
< 0.1%
ValueCountFrequency (%)
-1 922
3.4%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 8
 
< 0.1%
9 2
 
< 0.1%
10 6
 
< 0.1%
11 3
 
< 0.1%
12 5
 
< 0.1%
ValueCountFrequency (%)
-1 2114
18.3%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 6
 
0.1%
6 8
 
0.1%
7 10
 
0.1%
8 6
 
0.1%
9 7
 
0.1%
10 13
 
0.1%
11 12
 
0.1%
 TrainTest
Distinct3737
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5.70358255.6591442
 TrainTest
Minimum-1-1
Maximum3535
Zeros2610
Zeros (%)0.1%0.1%
Negative183967906
Negative (%)68.4%68.6%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:15.271540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q1-1-1
median-1-1
Q398
95-th percentile3334
Maximum3535
Range3636
Interquartile range (IQR)109

Descriptive statistics

 TrainTest
Standard deviation11.59530611.589643
Coefficient of variation (CV)2.03298652.0479498
Kurtosis0.701408360.7485214
Mean5.70358255.6591442
Median Absolute Deviation (MAD)00
Skewness1.49675521.5132063
Sum15331865199
Variance134.45112134.31983
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:15.379873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
-1 18396
68.4%
35 1001
 
3.7%
34 326
 
1.2%
33 291
 
1.1%
9 272
 
1.0%
6 267
 
1.0%
31 264
 
1.0%
29 264
 
1.0%
8 263
 
1.0%
5 255
 
0.9%
Other values (27) 5282
 
19.6%
ValueCountFrequency (%)
-1 7906
68.6%
35 445
 
3.9%
7 143
 
1.2%
34 142
 
1.2%
33 124
 
1.1%
29 122
 
1.1%
6 120
 
1.0%
9 119
 
1.0%
5 109
 
0.9%
31 106
 
0.9%
Other values (27) 2185
 
19.0%
ValueCountFrequency (%)
-1 18396
68.4%
0 26
 
0.1%
1 57
 
0.2%
2 127
 
0.5%
3 211
 
0.8%
4 217
 
0.8%
5 255
 
0.9%
6 267
 
1.0%
7 242
 
0.9%
8 263
 
1.0%
ValueCountFrequency (%)
-1 7906
68.6%
0 10
 
0.1%
1 19
 
0.2%
2 53
 
0.5%
3 86
 
0.7%
4 100
 
0.9%
5 109
 
0.9%
6 120
 
1.0%
7 143
 
1.2%
8 102
 
0.9%
ValueCountFrequency (%)
-1 7906
29.4%
0 10
 
< 0.1%
1 19
 
0.1%
2 53
 
0.2%
3 86
 
0.3%
4 100
 
0.4%
5 109
 
0.4%
6 120
 
0.4%
7 143
 
0.5%
8 102
 
0.4%
ValueCountFrequency (%)
-1 18396
159.7%
0 26
 
0.2%
1 57
 
0.5%
2 127
 
1.1%
3 211
 
1.8%
4 217
 
1.9%
5 255
 
2.2%
6 267
 
2.3%
7 242
 
2.1%
8 263
 
2.3%
 TrainTest
Distinct3737
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3.17171983.1141394
 TrainTest
Minimum-1-1
Maximum3535
Zeros9035
Zeros (%)0.3%0.3%
Negative183967906
Negative (%)68.4%68.6%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:15.487021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q1-1-1
median-1-1
Q333
95-th percentile2424
Maximum3535
Range3636
Interquartile range (IQR)44

Descriptive statistics

 TrainTest
Standard deviation8.12138518.0895214
Coefficient of variation (CV)2.5605622.5976748
Kurtosis3.49047663.6994481
Mean3.17171983.1141394
Median Absolute Deviation (MAD)00
Skewness2.10221072.1459487
Sum8525935878
Variance65.95689565.440356
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:15.595742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
-1 18396
68.4%
2 711
 
2.6%
3 638
 
2.4%
4 563
 
2.1%
6 448
 
1.7%
5 438
 
1.6%
1 394
 
1.5%
7 353
 
1.3%
8 346
 
1.3%
9 310
 
1.2%
Other values (27) 4284
 
15.9%
ValueCountFrequency (%)
-1 7906
68.6%
2 301
 
2.6%
3 281
 
2.4%
4 249
 
2.2%
5 223
 
1.9%
6 200
 
1.7%
1 163
 
1.4%
7 159
 
1.4%
8 147
 
1.3%
10 131
 
1.1%
Other values (27) 1761
 
15.3%
ValueCountFrequency (%)
-1 18396
68.4%
0 90
 
0.3%
1 394
 
1.5%
2 711
 
2.6%
3 638
 
2.4%
4 563
 
2.1%
5 438
 
1.6%
6 448
 
1.7%
7 353
 
1.3%
8 346
 
1.3%
ValueCountFrequency (%)
-1 7906
68.6%
0 35
 
0.3%
1 163
 
1.4%
2 301
 
2.6%
3 281
 
2.4%
4 249
 
2.2%
5 223
 
1.9%
6 200
 
1.7%
7 159
 
1.4%
8 147
 
1.3%
ValueCountFrequency (%)
-1 7906
29.4%
0 35
 
0.1%
1 163
 
0.6%
2 301
 
1.1%
3 281
 
1.0%
4 249
 
0.9%
5 223
 
0.8%
6 200
 
0.7%
7 159
 
0.6%
8 147
 
0.5%
ValueCountFrequency (%)
-1 18396
159.7%
0 90
 
0.8%
1 394
 
3.4%
2 711
 
6.2%
3 638
 
5.5%
4 563
 
4.9%
5 438
 
3.8%
6 448
 
3.9%
7 353
 
3.1%
8 346
 
3.0%

num_times_delinquent
Real number (ℝ)

 TrainTest
Distinct5548
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.73308281.7081851
 TrainTest
Minimum00
Maximum6574
Zeros183967906
Zeros (%)68.4%68.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:15.709838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile1010
Maximum6574
Range6574
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation4.50624514.5226371
Coefficient of variation (CV)2.60013262.6476271
Kurtosis36.68130444.355703
Mean1.73308281.7081851
Median Absolute Deviation (MAD)00
Skewness4.93783215.3499869
Sum4658719680
Variance20.30624520.454246
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:16.432151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18396
68.4%
1 2379
 
8.9%
2 1371
 
5.1%
3 948
 
3.5%
4 606
 
2.3%
5 499
 
1.9%
6 357
 
1.3%
7 335
 
1.2%
8 287
 
1.1%
9 229
 
0.9%
Other values (45) 1474
 
5.5%
ValueCountFrequency (%)
0 7906
68.6%
1 1010
 
8.8%
2 571
 
5.0%
3 418
 
3.6%
4 294
 
2.6%
5 182
 
1.6%
6 180
 
1.6%
7 125
 
1.1%
8 124
 
1.1%
9 107
 
0.9%
Other values (38) 604
 
5.2%
ValueCountFrequency (%)
0 18396
68.4%
1 2379
 
8.9%
2 1371
 
5.1%
3 948
 
3.5%
4 606
 
2.3%
5 499
 
1.9%
6 357
 
1.3%
7 335
 
1.2%
8 287
 
1.1%
9 229
 
0.9%
ValueCountFrequency (%)
0 7906
68.6%
1 1010
 
8.8%
2 571
 
5.0%
3 418
 
3.6%
4 294
 
2.6%
5 182
 
1.6%
6 180
 
1.6%
7 125
 
1.1%
8 124
 
1.1%
9 107
 
0.9%
ValueCountFrequency (%)
0 7906
29.4%
1 1010
 
3.8%
2 571
 
2.1%
3 418
 
1.6%
4 294
 
1.1%
5 182
 
0.7%
6 180
 
0.7%
7 125
 
0.5%
8 124
 
0.5%
9 107
 
0.4%
ValueCountFrequency (%)
0 18396
159.7%
1 2379
 
20.6%
2 1371
 
11.9%
3 948
 
8.2%
4 606
 
5.3%
5 499
 
4.3%
6 357
 
3.1%
7 335
 
2.9%
8 287
 
2.5%
9 229
 
2.0%

max_delinquency_level
Real number (ℝ)

 TrainTest
Distinct395293
Distinct (%)1.5%2.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean22.98080422.582762
 TrainTest
Minimum-1-1
Maximum900900
Zeros00
Zeros (%)0.0%0.0%
Negative183967906
Negative (%)68.4%68.6%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:16.550933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q1-1-1
median-1-1
Q32017
95-th percentile9593
Maximum900900
Range901901
Interquartile range (IQR)2118

Descriptive statistics

 TrainTest
Standard deviation78.87241778.257876
Coefficient of variation (CV)3.43209993.4653811
Kurtosis68.16619170.238933
Mean22.98080422.582762
Median Absolute Deviation (MAD)00
Skewness7.36943137.4863934
Sum617747260176
Variance6220.85816124.2951
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:16.660644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 18396
68.4%
3 375
 
1.4%
26 370
 
1.4%
30 369
 
1.4%
28 326
 
1.2%
25 305
 
1.1%
60 257
 
1.0%
27 248
 
0.9%
89 231
 
0.9%
24 221
 
0.8%
Other values (385) 5783
 
21.5%
ValueCountFrequency (%)
-1 7906
68.6%
26 187
 
1.6%
30 172
 
1.5%
3 170
 
1.5%
28 136
 
1.2%
25 119
 
1.0%
89 112
 
1.0%
60 107
 
0.9%
27 98
 
0.9%
29 89
 
0.8%
Other values (283) 2425
 
21.0%
ValueCountFrequency (%)
-1 18396
68.4%
1 152
 
0.6%
2 75
 
0.3%
3 375
 
1.4%
4 69
 
0.3%
5 95
 
0.4%
6 56
 
0.2%
7 95
 
0.4%
8 62
 
0.2%
9 86
 
0.3%
ValueCountFrequency (%)
-1 7906
68.6%
1 43
 
0.4%
2 47
 
0.4%
3 170
 
1.5%
4 28
 
0.2%
5 48
 
0.4%
6 32
 
0.3%
7 36
 
0.3%
8 27
 
0.2%
9 48
 
0.4%
ValueCountFrequency (%)
-1 7906
29.4%
1 43
 
0.2%
2 47
 
0.2%
3 170
 
0.6%
4 28
 
0.1%
5 48
 
0.2%
6 32
 
0.1%
7 36
 
0.1%
8 27
 
0.1%
9 48
 
0.2%
ValueCountFrequency (%)
-1 18396
159.7%
1 152
 
1.3%
2 75
 
0.7%
3 375
 
3.3%
4 69
 
0.6%
5 95
 
0.8%
6 56
 
0.5%
7 95
 
0.8%
8 62
 
0.5%
9 86
 
0.7%

max_recent_level_of_deliq
Real number (ℝ)

 TrainTest
Distinct270194
Distinct (%)1.0%1.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean14.50641714.580245
 TrainTest
Minimum00
Maximum900900
Zeros183967906
Zeros (%)68.4%68.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:16.772703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q31111
95-th percentile6059
Maximum900900
Range900900
Interquartile range (IQR)1111

Descriptive statistics

 TrainTest
Standard deviation58.00011960.113427
Coefficient of variation (CV)3.99823874.1229368
Kurtosis144.96978145.63771
Mean14.50641714.580245
Median Absolute Deviation (MAD)00
Skewness10.87755810.995845
Sum389947167979
Variance3364.01383613.6241
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:16.885609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18396
68.4%
30 632
 
2.4%
25 525
 
2.0%
26 520
 
1.9%
3 489
 
1.8%
28 456
 
1.7%
27 407
 
1.5%
29 345
 
1.3%
24 314
 
1.2%
60 222
 
0.8%
Other values (260) 4575
 
17.0%
ValueCountFrequency (%)
0 7906
68.6%
30 289
 
2.5%
26 227
 
2.0%
25 221
 
1.9%
3 208
 
1.8%
28 180
 
1.6%
27 168
 
1.5%
29 159
 
1.4%
24 134
 
1.2%
60 116
 
1.0%
Other values (184) 1913
 
16.6%
ValueCountFrequency (%)
0 18396
68.4%
1 211
 
0.8%
2 107
 
0.4%
3 489
 
1.8%
4 87
 
0.3%
5 112
 
0.4%
6 87
 
0.3%
7 155
 
0.6%
8 103
 
0.4%
9 126
 
0.5%
ValueCountFrequency (%)
0 7906
68.6%
1 63
 
0.5%
2 63
 
0.5%
3 208
 
1.8%
4 36
 
0.3%
5 57
 
0.5%
6 44
 
0.4%
7 62
 
0.5%
8 46
 
0.4%
9 67
 
0.6%
ValueCountFrequency (%)
0 7906
29.4%
1 63
 
0.2%
2 63
 
0.2%
3 208
 
0.8%
4 36
 
0.1%
5 57
 
0.2%
6 44
 
0.2%
7 62
 
0.2%
8 46
 
0.2%
9 67
 
0.2%
ValueCountFrequency (%)
0 18396
159.7%
1 211
 
1.8%
2 107
 
0.9%
3 489
 
4.2%
4 87
 
0.8%
5 112
 
1.0%
6 87
 
0.8%
7 155
 
1.3%
8 103
 
0.9%
9 126
 
1.1%

num_deliq_6mts
Real number (ℝ)

 TrainTest
Distinct1211
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.220713520.22237653
 TrainTest
Minimum00
Maximum1210
Zeros2404710269
Zeros (%)89.5%89.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:16.972888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum1210
Range1210
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.797072090.80062212
Coefficient of variation (CV)3.61134243.6002995
Kurtosis31.18830332.155318
Mean0.220713520.22237653
Median Absolute Deviation (MAD)00
Skewness4.9495495.0578781
Sum59332562
Variance0.635323910.64099578
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:17.050353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 24047
89.5%
1 1368
 
5.1%
2 649
 
2.4%
3 354
 
1.3%
4 266
 
1.0%
5 127
 
0.5%
6 30
 
0.1%
8 15
 
0.1%
7 15
 
0.1%
10 5
 
< 0.1%
Other values (2) 5
 
< 0.1%
ValueCountFrequency (%)
0 10269
89.1%
1 630
 
5.5%
2 293
 
2.5%
3 151
 
1.3%
4 81
 
0.7%
5 54
 
0.5%
6 24
 
0.2%
8 9
 
0.1%
7 5
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
0 24047
89.5%
1 1368
 
5.1%
2 649
 
2.4%
3 354
 
1.3%
4 266
 
1.0%
5 127
 
0.5%
6 30
 
0.1%
7 15
 
0.1%
8 15
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 10269
89.1%
1 630
 
5.5%
2 293
 
2.5%
3 151
 
1.3%
4 81
 
0.7%
5 54
 
0.5%
6 24
 
0.2%
7 5
 
< 0.1%
8 9
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 10269
38.2%
1 630
 
2.3%
2 293
 
1.1%
3 151
 
0.6%
4 81
 
0.3%
5 54
 
0.2%
6 24
 
0.1%
7 5
 
< 0.1%
8 9
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 24047
208.7%
1 1368
 
11.9%
2 649
 
5.6%
3 354
 
3.1%
4 266
 
2.3%
5 127
 
1.1%
6 30
 
0.3%
7 15
 
0.1%
8 15
 
0.1%
9 3
 
< 0.1%

num_deliq_12mts
Real number (ℝ)

 TrainTest
Distinct2322
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.54845430.55568093
 TrainTest
Minimum00
Maximum2422
Zeros220829429
Zeros (%)82.1%81.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:17.133026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile44
Maximum2422
Range2422
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation1.66132861.6759256
Coefficient of variation (CV)3.02911033.0159855
Kurtosis28.16266829.842615
Mean0.54845430.55568093
Median Absolute Deviation (MAD)00
Skewness4.61698064.7420555
Sum147436402
Variance2.76001262.8087266
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:17.221816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 22082
82.1%
1 1830
 
6.8%
2 971
 
3.6%
3 594
 
2.2%
4 361
 
1.3%
5 293
 
1.1%
6 221
 
0.8%
8 136
 
0.5%
7 123
 
0.5%
10 93
 
0.3%
Other values (13) 177
 
0.7%
ValueCountFrequency (%)
0 9429
81.8%
1 768
 
6.7%
2 448
 
3.9%
3 287
 
2.5%
4 160
 
1.4%
5 118
 
1.0%
6 93
 
0.8%
7 62
 
0.5%
8 44
 
0.4%
10 39
 
0.3%
Other values (12) 73
 
0.6%
ValueCountFrequency (%)
0 22082
82.1%
1 1830
 
6.8%
2 971
 
3.6%
3 594
 
2.2%
4 361
 
1.3%
5 293
 
1.1%
6 221
 
0.8%
7 123
 
0.5%
8 136
 
0.5%
9 66
 
0.2%
ValueCountFrequency (%)
0 9429
81.8%
1 768
 
6.7%
2 448
 
3.9%
3 287
 
2.5%
4 160
 
1.4%
5 118
 
1.0%
6 93
 
0.8%
7 62
 
0.5%
8 44
 
0.4%
9 17
 
0.1%
ValueCountFrequency (%)
0 9429
35.1%
1 768
 
2.9%
2 448
 
1.7%
3 287
 
1.1%
4 160
 
0.6%
5 118
 
0.4%
6 93
 
0.3%
7 62
 
0.2%
8 44
 
0.2%
9 17
 
0.1%
ValueCountFrequency (%)
0 22082
191.7%
1 1830
 
15.9%
2 971
 
8.4%
3 594
 
5.2%
4 361
 
3.1%
5 293
 
2.5%
6 221
 
1.9%
7 123
 
1.1%
8 136
 
1.2%
9 66
 
0.6%

num_deliq_6_12mts
Real number (ℝ)

 TrainTest
Distinct1515
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.327740780.3333044
 TrainTest
Minimum00
Maximum1717
Zeros234039974
Zeros (%)87.1%86.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:17.298452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum1717
Range1717
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation1.09195181.092752
Coefficient of variation (CV)3.33175453.2785405
Kurtosis28.01684328.862211
Mean0.327740780.3333044
Median Absolute Deviation (MAD)00
Skewness4.6526164.677795
Sum88103840
Variance1.19235881.1941069
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:17.379819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 23403
87.1%
1 1401
 
5.2%
2 793
 
3.0%
3 449
 
1.7%
4 317
 
1.2%
6 223
 
0.8%
5 185
 
0.7%
7 48
 
0.2%
8 23
 
0.1%
10 10
 
< 0.1%
Other values (5) 29
 
0.1%
ValueCountFrequency (%)
0 9974
86.6%
1 636
 
5.5%
2 353
 
3.1%
3 212
 
1.8%
4 138
 
1.2%
6 79
 
0.7%
5 75
 
0.7%
7 22
 
0.2%
8 11
 
0.1%
9 11
 
0.1%
Other values (5) 10
 
0.1%
ValueCountFrequency (%)
0 23403
87.1%
1 1401
 
5.2%
2 793
 
3.0%
3 449
 
1.7%
4 317
 
1.2%
5 185
 
0.7%
6 223
 
0.8%
7 48
 
0.2%
8 23
 
0.1%
9 10
 
< 0.1%
ValueCountFrequency (%)
0 9974
86.6%
1 636
 
5.5%
2 353
 
3.1%
3 212
 
1.8%
4 138
 
1.2%
5 75
 
0.7%
6 79
 
0.7%
7 22
 
0.2%
8 11
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
0 9974
37.1%
1 636
 
2.4%
2 353
 
1.3%
3 212
 
0.8%
4 138
 
0.5%
5 75
 
0.3%
6 79
 
0.3%
7 22
 
0.1%
8 11
 
< 0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
0 23403
203.1%
1 1401
 
12.2%
2 793
 
6.9%
3 449
 
3.9%
4 317
 
2.8%
5 185
 
1.6%
6 223
 
1.9%
7 48
 
0.4%
8 23
 
0.2%
9 10
 
0.1%

max_deliq_6mts
Real number (ℝ)

 TrainTest
Distinct157126
Distinct (%)0.6%1.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean4.18314054.4697509
 TrainTest
Minimum-1-1
Maximum900900
Zeros179227633
Zeros (%)66.7%66.3%
Negative61252636
Negative (%)22.8%22.9%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:17.484566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q300
95-th percentile2626
Maximum900900
Range901901
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation31.92389535.656285
Coefficient of variation (CV)7.63156187.977242
Kurtosis530.47284468.76115
Mean4.18314054.4697509
Median Absolute Deviation (MAD)00
Skewness20.6394519.897142
Sum11244751496
Variance1019.13511271.3706
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:17.597527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17922
66.7%
-1 6125
 
22.8%
3 329
 
1.2%
28 179
 
0.7%
26 169
 
0.6%
27 138
 
0.5%
25 132
 
0.5%
24 110
 
0.4%
10 102
 
0.4%
30 99
 
0.4%
Other values (147) 1576
 
5.9%
ValueCountFrequency (%)
0 7633
66.3%
-1 2636
 
22.9%
3 148
 
1.3%
25 70
 
0.6%
28 69
 
0.6%
26 69
 
0.6%
30 65
 
0.6%
24 53
 
0.5%
27 52
 
0.5%
29 42
 
0.4%
Other values (116) 684
 
5.9%
ValueCountFrequency (%)
-1 6125
 
22.8%
0 17922
66.7%
1 68
 
0.3%
2 32
 
0.1%
3 329
 
1.2%
4 33
 
0.1%
5 33
 
0.1%
6 29
 
0.1%
7 33
 
0.1%
8 39
 
0.1%
ValueCountFrequency (%)
-1 2636
 
22.9%
0 7633
66.3%
1 24
 
0.2%
2 26
 
0.2%
3 148
 
1.3%
4 7
 
0.1%
5 18
 
0.2%
6 13
 
0.1%
7 11
 
0.1%
8 20
 
0.2%
ValueCountFrequency (%)
-1 2636
 
9.8%
0 7633
28.4%
1 24
 
0.1%
2 26
 
0.1%
3 148
 
0.6%
4 7
 
< 0.1%
5 18
 
0.1%
6 13
 
< 0.1%
7 11
 
< 0.1%
8 20
 
0.1%
ValueCountFrequency (%)
-1 6125
 
53.2%
0 17922
155.6%
1 68
 
0.6%
2 32
 
0.3%
3 329
 
2.9%
4 33
 
0.3%
5 33
 
0.3%
6 29
 
0.3%
7 33
 
0.3%
8 39
 
0.3%

max_deliq_12mts
Real number (ℝ)

 TrainTest
Distinct220171
Distinct (%)0.8%1.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean8.0159228.2846975
 TrainTest
Minimum-1-1
Maximum900900
Zeros169627201
Zeros (%)63.1%62.5%
Negative51202228
Negative (%)19.0%19.3%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:17.712178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q300
95-th percentile4549
Maximum900900
Range901901
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation40.73308342.019415
Coefficient of variation (CV)5.08152195.0719311
Kurtosis278.69705276.49202
Mean8.0159228.2846975
Median Absolute Deviation (MAD)00
Skewness14.56399914.608302
Sum21547695448
Variance1659.1841765.6313
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:17.824889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16962
63.1%
-1 5120
 
19.0%
3 381
 
1.4%
28 288
 
1.1%
26 263
 
1.0%
27 240
 
0.9%
25 210
 
0.8%
30 188
 
0.7%
29 162
 
0.6%
24 157
 
0.6%
Other values (210) 2910
 
10.8%
ValueCountFrequency (%)
0 7201
62.5%
-1 2228
 
19.3%
3 164
 
1.4%
26 124
 
1.1%
28 121
 
1.1%
25 99
 
0.9%
30 89
 
0.8%
27 82
 
0.7%
29 80
 
0.7%
24 76
 
0.7%
Other values (161) 1257
 
10.9%
ValueCountFrequency (%)
-1 5120
 
19.0%
0 16962
63.1%
1 102
 
0.4%
2 56
 
0.2%
3 381
 
1.4%
4 45
 
0.2%
5 63
 
0.2%
6 46
 
0.2%
7 86
 
0.3%
8 58
 
0.2%
ValueCountFrequency (%)
-1 2228
 
19.3%
0 7201
62.5%
1 31
 
0.3%
2 43
 
0.4%
3 164
 
1.4%
4 18
 
0.2%
5 38
 
0.3%
6 27
 
0.2%
7 22
 
0.2%
8 21
 
0.2%
ValueCountFrequency (%)
-1 2228
 
8.3%
0 7201
26.8%
1 31
 
0.1%
2 43
 
0.2%
3 164
 
0.6%
4 18
 
0.1%
5 38
 
0.1%
6 27
 
0.1%
7 22
 
0.1%
8 21
 
0.1%
ValueCountFrequency (%)
-1 5120
 
44.4%
0 16962
147.2%
1 102
 
0.9%
2 56
 
0.5%
3 381
 
3.3%
4 45
 
0.4%
5 63
 
0.5%
6 46
 
0.4%
7 86
 
0.7%
8 58
 
0.5%

num_times_30p_dpd
Real number (ℝ)

 TrainTest
Distinct4439
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.791376810.79177155
 TrainTest
Minimum00
Maximum6059
Zeros224969667
Zeros (%)83.7%83.9%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:17.931007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile55
Maximum6059
Range6059
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation2.99574383.0448821
Coefficient of variation (CV)3.78548343.8456574
Kurtosis76.29862982.653981
Mean0.791376810.79177155
Median Absolute Deviation (MAD)00
Skewness7.1737927.4978316
Sum212739122
Variance8.97448079.2713073
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:18.040722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 22496
83.7%
1 1326
 
4.9%
2 840
 
3.1%
3 485
 
1.8%
4 291
 
1.1%
5 248
 
0.9%
6 173
 
0.6%
7 151
 
0.6%
8 132
 
0.5%
10 109
 
0.4%
Other values (34) 630
 
2.3%
ValueCountFrequency (%)
0 9667
83.9%
1 545
 
4.7%
2 361
 
3.1%
3 212
 
1.8%
4 113
 
1.0%
5 98
 
0.9%
6 81
 
0.7%
8 79
 
0.7%
7 59
 
0.5%
10 44
 
0.4%
Other values (29) 262
 
2.3%
ValueCountFrequency (%)
0 22496
83.7%
1 1326
 
4.9%
2 840
 
3.1%
3 485
 
1.8%
4 291
 
1.1%
5 248
 
0.9%
6 173
 
0.6%
7 151
 
0.6%
8 132
 
0.5%
9 104
 
0.4%
ValueCountFrequency (%)
0 9667
83.9%
1 545
 
4.7%
2 361
 
3.1%
3 212
 
1.8%
4 113
 
1.0%
5 98
 
0.9%
6 81
 
0.7%
7 59
 
0.5%
8 79
 
0.7%
9 38
 
0.3%
ValueCountFrequency (%)
0 9667
36.0%
1 545
 
2.0%
2 361
 
1.3%
3 212
 
0.8%
4 113
 
0.4%
5 98
 
0.4%
6 81
 
0.3%
7 59
 
0.2%
8 79
 
0.3%
9 38
 
0.1%
ValueCountFrequency (%)
0 22496
195.3%
1 1326
 
11.5%
2 840
 
7.3%
3 485
 
4.2%
4 291
 
2.5%
5 248
 
2.2%
6 173
 
1.5%
7 151
 
1.3%
8 132
 
1.1%
9 104
 
0.9%

num_times_60p_dpd
Real number (ℝ)

 TrainTest
Distinct4136
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.472080650.47903828
 TrainTest
Minimum00
Maximum4952
Zeros2418510396
Zeros (%)90.0%90.2%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:18.147848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum4952
Range4952
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation2.35631192.4479156
Coefficient of variation (CV)4.99133345.1100625
Kurtosis110.27747114.69481
Mean0.472080650.47903828
Median Absolute Deviation (MAD)00
Skewness8.9975919.2481878
Sum126905519
Variance5.5522065.9922906
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:18.252026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 24185
90.0%
1 942
 
3.5%
2 461
 
1.7%
3 241
 
0.9%
4 204
 
0.8%
5 150
 
0.6%
6 104
 
0.4%
7 87
 
0.3%
8 84
 
0.3%
9 59
 
0.2%
Other values (31) 364
 
1.4%
ValueCountFrequency (%)
0 10396
90.2%
1 380
 
3.3%
2 195
 
1.7%
3 101
 
0.9%
4 80
 
0.7%
5 57
 
0.5%
6 53
 
0.5%
7 42
 
0.4%
8 29
 
0.3%
10 25
 
0.2%
Other values (26) 163
 
1.4%
ValueCountFrequency (%)
0 24185
90.0%
1 942
 
3.5%
2 461
 
1.7%
3 241
 
0.9%
4 204
 
0.8%
5 150
 
0.6%
6 104
 
0.4%
7 87
 
0.3%
8 84
 
0.3%
9 59
 
0.2%
ValueCountFrequency (%)
0 10396
90.2%
1 380
 
3.3%
2 195
 
1.7%
3 101
 
0.9%
4 80
 
0.7%
5 57
 
0.5%
6 53
 
0.5%
7 42
 
0.4%
8 29
 
0.3%
9 24
 
0.2%
ValueCountFrequency (%)
0 10396
38.7%
1 380
 
1.4%
2 195
 
0.7%
3 101
 
0.4%
4 80
 
0.3%
5 57
 
0.2%
6 53
 
0.2%
7 42
 
0.2%
8 29
 
0.1%
9 24
 
0.1%
ValueCountFrequency (%)
0 24185
209.9%
1 942
 
8.2%
2 461
 
4.0%
3 241
 
2.1%
4 204
 
1.8%
5 150
 
1.3%
6 104
 
0.9%
7 87
 
0.8%
8 84
 
0.7%
9 59
 
0.5%

num_std
Real number (ℝ)

 TrainTest
Distinct194174
Distinct (%)0.7%1.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean9.2317259.5550733
 TrainTest
Minimum00
Maximum331422
Zeros165777061
Zeros (%)61.7%61.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:18.364223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q399
95-th percentile4850
Maximum331422
Range331422
Interquartile range (IQR)99

Descriptive statistics

 TrainTest
Standard deviation21.19424222.120938
Coefficient of variation (CV)2.29580522.3150987
Kurtosis30.19071337.521028
Mean9.2317259.5550733
Median Absolute Deviation (MAD)00
Skewness4.41103044.7226004
Sum248158110084
Variance449.19589489.33588
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:18.474655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16577
61.7%
1 772
 
2.9%
2 530
 
2.0%
3 518
 
1.9%
4 392
 
1.5%
6 352
 
1.3%
7 314
 
1.2%
5 311
 
1.2%
11 294
 
1.1%
8 287
 
1.1%
Other values (184) 6534
 
24.3%
ValueCountFrequency (%)
0 7061
61.3%
1 341
 
3.0%
2 230
 
2.0%
3 229
 
2.0%
4 168
 
1.5%
8 146
 
1.3%
6 144
 
1.2%
5 138
 
1.2%
9 130
 
1.1%
7 126
 
1.1%
Other values (164) 2808
 
24.4%
ValueCountFrequency (%)
0 16577
61.7%
1 772
 
2.9%
2 530
 
2.0%
3 518
 
1.9%
4 392
 
1.5%
5 311
 
1.2%
6 352
 
1.3%
7 314
 
1.2%
8 287
 
1.1%
9 255
 
0.9%
ValueCountFrequency (%)
0 7061
61.3%
1 341
 
3.0%
2 230
 
2.0%
3 229
 
2.0%
4 168
 
1.5%
5 138
 
1.2%
6 144
 
1.2%
7 126
 
1.1%
8 146
 
1.3%
9 130
 
1.1%
ValueCountFrequency (%)
0 7061
26.3%
1 341
 
1.3%
2 230
 
0.9%
3 229
 
0.9%
4 168
 
0.6%
5 138
 
0.5%
6 144
 
0.5%
7 126
 
0.5%
8 146
 
0.5%
9 130
 
0.5%
ValueCountFrequency (%)
0 16577
143.9%
1 772
 
6.7%
2 530
 
4.6%
3 518
 
4.5%
4 392
 
3.4%
5 311
 
2.7%
6 352
 
3.1%
7 314
 
2.7%
8 287
 
2.5%
9 255
 
2.2%

num_std_6mts
Real number (ℝ)

 TrainTest
Distinct4639
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.48766791.5252148
 TrainTest
Minimum00
Maximum6060
Zeros195298393
Zeros (%)72.6%72.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:18.581700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile88
Maximum6060
Range6060
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation3.3645573.4703722
Coefficient of variation (CV)2.26163192.2753334
Kurtosis32.24698828.439431
Mean1.48766791.5252148
Median Absolute Deviation (MAD)00
Skewness4.25419284.1024165
Sum3999017572
Variance11.32024412.043483
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:18.687335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 19529
72.6%
4 1963
 
7.3%
5 1350
 
5.0%
1 718
 
2.7%
3 681
 
2.5%
2 649
 
2.4%
8 370
 
1.4%
10 326
 
1.2%
6 248
 
0.9%
7 219
 
0.8%
Other values (36) 828
 
3.1%
ValueCountFrequency (%)
0 8393
72.8%
4 763
 
6.6%
5 586
 
5.1%
3 304
 
2.6%
1 304
 
2.6%
2 258
 
2.2%
8 169
 
1.5%
10 136
 
1.2%
6 131
 
1.1%
7 94
 
0.8%
Other values (29) 383
 
3.3%
ValueCountFrequency (%)
0 19529
72.6%
1 718
 
2.7%
2 649
 
2.4%
3 681
 
2.5%
4 1963
 
7.3%
5 1350
 
5.0%
6 248
 
0.9%
7 219
 
0.8%
8 370
 
1.4%
9 189
 
0.7%
ValueCountFrequency (%)
0 8393
72.8%
1 304
 
2.6%
2 258
 
2.2%
3 304
 
2.6%
4 763
 
6.6%
5 586
 
5.1%
6 131
 
1.1%
7 94
 
0.8%
8 169
 
1.5%
9 63
 
0.5%
ValueCountFrequency (%)
0 8393
31.2%
1 304
 
1.1%
2 258
 
1.0%
3 304
 
1.1%
4 763
 
2.8%
5 586
 
2.2%
6 131
 
0.5%
7 94
 
0.3%
8 169
 
0.6%
9 63
 
0.2%
ValueCountFrequency (%)
0 19529
169.5%
1 718
 
6.2%
2 649
 
5.6%
3 681
 
5.9%
4 1963
 
17.0%
5 1350
 
11.7%
6 248
 
2.2%
7 219
 
1.9%
8 370
 
3.2%
9 189
 
1.6%

num_std_12mts
Real number (ℝ)

 TrainTest
Distinct8277
Distinct (%)0.3%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3.3351813.4004861
 TrainTest
Minimum00
Maximum122107
Zeros186587999
Zeros (%)69.4%69.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:18.801778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q333
95-th percentile1818
Maximum122107
Range122107
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation7.52176917.7534163
Coefficient of variation (CV)2.25528062.2800906
Kurtosis28.62194528.022455
Mean3.3351813.4004861
Median Absolute Deviation (MAD)00
Skewness4.14907684.2133651
Sum8965339177
Variance56.5770160.115465
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:19.088282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18658
69.4%
10 1105
 
4.1%
11 786
 
2.9%
1 704
 
2.6%
2 540
 
2.0%
8 441
 
1.6%
3 432
 
1.6%
4 430
 
1.6%
9 428
 
1.6%
7 407
 
1.5%
Other values (72) 2950
 
11.0%
ValueCountFrequency (%)
0 7999
69.4%
10 434
 
3.8%
11 350
 
3.0%
1 284
 
2.5%
2 220
 
1.9%
9 199
 
1.7%
4 199
 
1.7%
8 181
 
1.6%
3 179
 
1.6%
5 177
 
1.5%
Other values (67) 1299
 
11.3%
ValueCountFrequency (%)
0 18658
69.4%
1 704
 
2.6%
2 540
 
2.0%
3 432
 
1.6%
4 430
 
1.6%
5 344
 
1.3%
6 380
 
1.4%
7 407
 
1.5%
8 441
 
1.6%
9 428
 
1.6%
ValueCountFrequency (%)
0 7999
69.4%
1 284
 
2.5%
2 220
 
1.9%
3 179
 
1.6%
4 199
 
1.7%
5 177
 
1.5%
6 164
 
1.4%
7 165
 
1.4%
8 181
 
1.6%
9 199
 
1.7%
ValueCountFrequency (%)
0 7999
29.8%
1 284
 
1.1%
2 220
 
0.8%
3 179
 
0.7%
4 199
 
0.7%
5 177
 
0.7%
6 164
 
0.6%
7 165
 
0.6%
8 181
 
0.7%
9 199
 
0.7%
ValueCountFrequency (%)
0 18658
161.9%
1 704
 
6.1%
2 540
 
4.7%
3 432
 
3.7%
4 430
 
3.7%
5 344
 
3.0%
6 380
 
3.3%
7 407
 
3.5%
8 441
 
3.8%
9 428
 
3.7%

num_sub
Real number (ℝ)

 TrainTest
Distinct2219
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0609724340.054509157
 TrainTest
Minimum00
Maximum4022
Zeros2656211384
Zeros (%)98.8%98.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:19.186715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum4022
Range4022
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.789087270.70844167
Coefficient of variation (CV)12.94170512.996746
Kurtosis728.60309436.97611
Mean0.0609724340.054509157
Median Absolute Deviation (MAD)00
Skewness22.11940819.007643
Sum1639628
Variance0.622658710.5018896
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:19.275135image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 26562
98.8%
1 83
 
0.3%
2 45
 
0.2%
6 36
 
0.1%
3 32
 
0.1%
4 19
 
0.1%
7 18
 
0.1%
10 16
 
0.1%
5 15
 
0.1%
8 13
 
< 0.1%
Other values (12) 42
 
0.2%
ValueCountFrequency (%)
0 11384
98.8%
1 43
 
0.4%
2 19
 
0.2%
3 18
 
0.2%
5 12
 
0.1%
4 7
 
0.1%
10 7
 
0.1%
8 6
 
0.1%
7 6
 
0.1%
6 5
 
< 0.1%
Other values (9) 14
 
0.1%
ValueCountFrequency (%)
0 26562
98.8%
1 83
 
0.3%
2 45
 
0.2%
3 32
 
0.1%
4 19
 
0.1%
5 15
 
0.1%
6 36
 
0.1%
7 18
 
0.1%
8 13
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
0 11384
98.8%
1 43
 
0.4%
2 19
 
0.2%
3 18
 
0.2%
4 7
 
0.1%
5 12
 
0.1%
6 5
 
< 0.1%
7 6
 
0.1%
8 6
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 11384
42.3%
1 43
 
0.2%
2 19
 
0.1%
3 18
 
0.1%
4 7
 
< 0.1%
5 12
 
< 0.1%
6 5
 
< 0.1%
7 6
 
< 0.1%
8 6
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 26562
230.6%
1 83
 
0.7%
2 45
 
0.4%
3 32
 
0.3%
4 19
 
0.2%
5 15
 
0.1%
6 36
 
0.3%
7 18
 
0.2%
8 13
 
0.1%
9 8
 
0.1%
 TrainTest
Distinct65
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26856 
1
 
12
2
 
6
3
 
4
4
 
2
0
11514 
1
 
3
5
 
2
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11521
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique12 ?
Unique (%)< 0.1%< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26856
99.9%
1 12
 
< 0.1%
2 6
 
< 0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 11514
99.9%
1 3
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Length

2025-03-10T23:50:19.360648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:19.426064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:19.495316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 11514
99.9%
1 3
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11514
99.9%
1 3
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11514
99.9%
1 3
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11514
99.9%
1 3
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11514
99.9%
1 3
 
< 0.1%
5 2
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%

num_sub_12mts
Real number (ℝ)

 TrainTest
Distinct1210
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.00900264130.008245812
 TrainTest
Minimum00
Maximum1211
Zeros2680011489
Zeros (%)99.7%99.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:19.568482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum1211
Range1211
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.211272840.2129115
Coefficient of variation (CV)23.46787225.820562
Kurtosis1256.20491507.6331
Mean0.00900264130.008245812
Median Absolute Deviation (MAD)00
Skewness32.33341136.144618
Sum24295
Variance0.0446362110.045331306
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:19.645455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 26800
99.7%
1 27
 
0.1%
2 25
 
0.1%
6 12
 
< 0.1%
3 5
 
< 0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
11 1
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 11489
99.7%
1 14
 
0.1%
2 6
 
0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
8 2
 
< 0.1%
5 1
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 26800
99.7%
1 27
 
0.1%
2 25
 
0.1%
3 5
 
< 0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
6 12
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 11489
99.7%
1 14
 
0.1%
2 6
 
0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 11489
42.7%
1 14
 
0.1%
2 6
 
< 0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 26800
232.6%
1 27
 
0.2%
2 25
 
0.2%
3 5
 
< 0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
6 12
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%

num_dbt
Real number (ℝ)

 TrainTest
Distinct2317
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.023139020.020657929
 TrainTest
Minimum00
Maximum3226
Zeros2679011489
Zeros (%)99.7%99.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:19.723678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum3226
Range3226
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.581958650.58771298
Coefficient of variation (CV)25.15053128.449753
Kurtosis1356.76121445.1804
Mean0.023139020.020657929
Median Absolute Deviation (MAD)00
Skewness34.12793436.478921
Sum622238
Variance0.338675880.34540655
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:19.807393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 26790
99.7%
1 32
 
0.1%
2 8
 
< 0.1%
5 8
 
< 0.1%
6 6
 
< 0.1%
13 5
 
< 0.1%
9 5
 
< 0.1%
3 4
 
< 0.1%
11 3
 
< 0.1%
8 2
 
< 0.1%
Other values (13) 18
 
0.1%
ValueCountFrequency (%)
0 11489
99.7%
1 10
 
0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
9 2
 
< 0.1%
26 2
 
< 0.1%
6 1
 
< 0.1%
14 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
Other values (7) 7
 
0.1%
ValueCountFrequency (%)
0 26790
99.7%
1 32
 
0.1%
2 8
 
< 0.1%
3 4
 
< 0.1%
5 8
 
< 0.1%
6 6
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 5
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 11489
99.7%
1 10
 
0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 11489
42.7%
1 10
 
< 0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 26790
232.5%
1 32
 
0.3%
2 8
 
0.1%
3 4
 
< 0.1%
5 8
 
0.1%
6 6
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 5
 
< 0.1%
10 1
 
< 0.1%

num_dbt_6mts
Categorical

 TrainTest
Distinct45
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26873 
4
 
4
5
 
2
3
 
2
0
11517 
5
 
1
3
 
1
2
 
1
4
 
1

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters45
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique04 ?
Unique (%)0.0%< 0.1%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 26873
> 99.9%
4 4
 
< 0.1%
5 2
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 11517
> 99.9%
5 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Length

2025-03-10T23:50:19.886740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:19.950782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:20.013814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 26873
> 99.9%
4 4
 
< 0.1%
5 2
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 11517
> 99.9%
5 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 26873
> 99.9%
4 4
 
< 0.1%
5 2
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 11517
> 99.9%
5 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26873
> 99.9%
4 4
 
< 0.1%
5 2
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 11517
> 99.9%
5 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26873
> 99.9%
4 4
 
< 0.1%
5 2
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 11517
> 99.9%
5 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26873
> 99.9%
4 4
 
< 0.1%
5 2
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 11517
> 99.9%
5 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

num_dbt_12mts
Real number (ℝ)

 TrainTest
Distinct98
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.00342249170.0044266991
 TrainTest
Minimum00
Maximum1111
Zeros2686411512
Zeros (%)99.9%99.9%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:20.077940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum1111
Range1111
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.167448150.18791025
Coefficient of variation (CV)48.925842.449295
Kurtosis3305.29252489.1742
Mean0.00342249170.0044266991
Median Absolute Deviation (MAD)00
Skewness56.20134448.565995
Sum9251
Variance0.0280388810.035310264
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:20.147394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 26864
99.9%
2 4
 
< 0.1%
1 3
 
< 0.1%
9 3
 
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 11512
99.9%
4 2
 
< 0.1%
2 2
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
1 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 26864
99.9%
1 3
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
0 11512
99.9%
1 1
 
< 0.1%
2 2
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 11512
42.8%
1 1
 
< 0.1%
2 2
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 26864
233.2%
1 3
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%

num_lss
Real number (ℝ)

 TrainTest
Distinct1910
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0213905730.0075514278
 TrainTest
Minimum00
Maximum7214
Zeros2682311501
Zeros (%)99.8%99.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:20.218762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum7214
Range7214
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.779210440.25213378
Coefficient of variation (CV)36.42774933.388888
Kurtosis5747.01511974.8213
Mean0.0213905730.0075514278
Median Absolute Deviation (MAD)00
Skewness68.32179642.501132
Sum57587
Variance0.607168910.063571443
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:20.297763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 26823
99.8%
1 9
 
< 0.1%
3 7
 
< 0.1%
8 6
 
< 0.1%
4 5
 
< 0.1%
9 5
 
< 0.1%
7 5
 
< 0.1%
11 4
 
< 0.1%
2 3
 
< 0.1%
15 2
 
< 0.1%
Other values (9) 12
 
< 0.1%
ValueCountFrequency (%)
0 11501
99.8%
1 8
 
0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%
2 2
 
< 0.1%
13 1
 
< 0.1%
10 1
 
< 0.1%
14 1
 
< 0.1%
4 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 26823
99.8%
1 9
 
< 0.1%
2 3
 
< 0.1%
3 7
 
< 0.1%
4 5
 
< 0.1%
6 1
 
< 0.1%
7 5
 
< 0.1%
8 6
 
< 0.1%
9 5
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
0 11501
99.8%
1 8
 
0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 11501
42.8%
1 8
 
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 26823
232.8%
1 9
 
0.1%
2 3
 
< 0.1%
3 7
 
0.1%
4 5
 
< 0.1%
6 1
 
< 0.1%
7 5
 
< 0.1%
8 6
 
0.1%
9 5
 
< 0.1%
10 2
 
< 0.1%

num_lss_6mts
Categorical

 TrainTest
Distinct54
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26875 
4
 
2
12
 
2
5
 
1
2
 
1
0
11518 
4
 
1
3
 
1
5
 
1

Length

 TrainTest
Max length21
Median length11
Mean length1.00007441
Min length11

Characters and Unicode

 TrainTest
Total characters2688311521
Distinct characters54
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique23 ?
Unique (%)< 0.1%< 0.1%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 26875
> 99.9%
4 2
 
< 0.1%
12 2
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%

Length

2025-03-10T23:50:20.391979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:20.461665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:20.523258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 26875
> 99.9%
4 2
 
< 0.1%
12 2
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 26875
> 99.9%
2 3
 
< 0.1%
4 2
 
< 0.1%
1 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26883
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26875
> 99.9%
2 3
 
< 0.1%
4 2
 
< 0.1%
1 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26883
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26875
> 99.9%
2 3
 
< 0.1%
4 2
 
< 0.1%
1 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26883
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26875
> 99.9%
2 3
 
< 0.1%
4 2
 
< 0.1%
1 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
 TrainTest
Distinct84
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
26868 
1
 
4
10
 
3
30
 
2
5
 
1
Other values (3)
 
3
0
11518 
4
 
1
3
 
1
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11521
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique43 ?
Unique (%)< 0.1%< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26868
> 99.9%
1 4
 
< 0.1%
10 3
 
< 0.1%
30 2
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%
2 1
 
< 0.1%
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%

Length

2025-03-10T23:50:20.591352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:20.661509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:20.729649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11518
> 99.9%
4 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%

recent_level_of_deliq
Real number (ℝ)

 TrainTest
Distinct239167
Distinct (%)0.9%1.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean11.94743512.046263
 TrainTest
Minimum00
Maximum900900
Zeros183967906
Zeros (%)68.4%68.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:20.822848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q388
95-th percentile4749
Maximum900900
Range900900
Interquartile range (IQR)88

Descriptive statistics

 TrainTest
Standard deviation50.99981252.557986
Coefficient of variation (CV)4.2686834.3630115
Kurtosis196.32812195.79338
Mean11.94743512.046263
Median Absolute Deviation (MAD)00
Skewness12.68526212.743776
Sum321159138785
Variance2600.98082762.3419
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:20.935954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18396
68.4%
3 692
 
2.6%
30 574
 
2.1%
25 538
 
2.0%
26 505
 
1.9%
28 414
 
1.5%
27 384
 
1.4%
24 342
 
1.3%
29 311
 
1.2%
1 263
 
1.0%
Other values (229) 4462
 
16.6%
ValueCountFrequency (%)
0 7906
68.6%
3 295
 
2.6%
30 266
 
2.3%
25 224
 
1.9%
26 209
 
1.8%
28 170
 
1.5%
27 161
 
1.4%
29 159
 
1.4%
24 136
 
1.2%
10 103
 
0.9%
Other values (157) 1892
 
16.4%
ValueCountFrequency (%)
0 18396
68.4%
1 263
 
1.0%
2 155
 
0.6%
3 692
 
2.6%
4 107
 
0.4%
5 149
 
0.6%
6 125
 
0.5%
7 187
 
0.7%
8 141
 
0.5%
9 147
 
0.5%
ValueCountFrequency (%)
0 7906
68.6%
1 82
 
0.7%
2 94
 
0.8%
3 295
 
2.6%
4 40
 
0.3%
5 71
 
0.6%
6 52
 
0.5%
7 71
 
0.6%
8 57
 
0.5%
9 85
 
0.7%
ValueCountFrequency (%)
0 7906
29.4%
1 82
 
0.3%
2 94
 
0.3%
3 295
 
1.1%
4 40
 
0.1%
5 71
 
0.3%
6 52
 
0.2%
7 71
 
0.3%
8 57
 
0.2%
9 85
 
0.3%
ValueCountFrequency (%)
0 18396
159.7%
1 263
 
2.3%
2 155
 
1.3%
3 692
 
6.0%
4 107
 
0.9%
5 149
 
1.3%
6 125
 
1.1%
7 187
 
1.6%
8 141
 
1.2%
9 147
 
1.3%

tot_enq
Real number (ℝ)

 TrainTest
Distinct7768
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5.44909045.2767121
 TrainTest
Minimum-1-1
Maximum17694
Zeros00
Zeros (%)0.0%0.0%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:21.048842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q111
median33
Q377
95-th percentile1918
Maximum17694
Range17795
Interquartile range (IQR)66

Descriptive statistics

 TrainTest
Standard deviation7.65258137.0386942
Coefficient of variation (CV)1.40437771.3339167
Kurtosis37.79163318.401522
Mean5.44909045.2767121
Median Absolute Deviation (MAD)22
Skewness4.20457183.3396313
Sum14647760793
Variance58.56200149.543215
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:21.173503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4555
16.9%
2 3834
14.3%
3 3015
11.2%
-1 2986
11.1%
4 2360
8.8%
5 1741
 
6.5%
6 1378
 
5.1%
7 1028
 
3.8%
8 815
 
3.0%
9 765
 
2.8%
Other values (67) 4404
16.4%
ValueCountFrequency (%)
1 2043
17.7%
2 1602
13.9%
3 1319
11.4%
-1 1227
10.7%
4 992
8.6%
5 786
 
6.8%
6 625
 
5.4%
7 421
 
3.7%
8 383
 
3.3%
9 299
 
2.6%
Other values (58) 1824
15.8%
ValueCountFrequency (%)
-1 2986
11.1%
1 4555
16.9%
2 3834
14.3%
3 3015
11.2%
4 2360
8.8%
5 1741
 
6.5%
6 1378
 
5.1%
7 1028
 
3.8%
8 815
 
3.0%
9 765
 
2.8%
ValueCountFrequency (%)
-1 1227
10.7%
1 2043
17.7%
2 1602
13.9%
3 1319
11.4%
4 992
8.6%
5 786
 
6.8%
6 625
 
5.4%
7 421
 
3.7%
8 383
 
3.3%
9 299
 
2.6%
ValueCountFrequency (%)
-1 1227
4.6%
1 2043
7.6%
2 1602
6.0%
3 1319
4.9%
4 992
3.7%
5 786
 
2.9%
6 625
 
2.3%
7 421
 
1.6%
8 383
 
1.4%
9 299
 
1.1%
ValueCountFrequency (%)
-1 2986
25.9%
1 4555
39.5%
2 3834
33.3%
3 3015
26.2%
4 2360
20.5%
5 1741
 
15.1%
6 1378
 
12.0%
7 1028
 
8.9%
8 815
 
7.1%
9 765
 
6.6%

CC_enq
Real number (ℝ)

 TrainTest
Distinct3327
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.639447940.62329659
 TrainTest
Minimum-1-1
Maximum3935
Zeros175587590
Zeros (%)65.3%65.9%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:21.277719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q300
95-th percentile44
Maximum3935
Range4036
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation2.29946882.1946729
Coefficient of variation (CV)3.5960223.5210732
Kurtosis45.79284638.170188
Mean0.639447940.62329659
Median Absolute Deviation (MAD)00
Skewness5.4406845.0580499
Sum171897181
Variance5.28755694.8165892
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:21.376819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 17558
65.3%
-1 2986
 
11.1%
1 2798
 
10.4%
2 1210
 
4.5%
3 647
 
2.4%
4 410
 
1.5%
5 268
 
1.0%
6 219
 
0.8%
8 146
 
0.5%
7 143
 
0.5%
Other values (23) 496
 
1.8%
ValueCountFrequency (%)
0 7590
65.9%
-1 1227
 
10.7%
1 1199
 
10.4%
2 504
 
4.4%
3 290
 
2.5%
4 181
 
1.6%
5 123
 
1.1%
6 100
 
0.9%
7 65
 
0.6%
8 47
 
0.4%
Other values (17) 195
 
1.7%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 17558
65.3%
1 2798
 
10.4%
2 1210
 
4.5%
3 647
 
2.4%
4 410
 
1.5%
5 268
 
1.0%
6 219
 
0.8%
7 143
 
0.5%
8 146
 
0.5%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 7590
65.9%
1 1199
 
10.4%
2 504
 
4.4%
3 290
 
2.5%
4 181
 
1.6%
5 123
 
1.1%
6 100
 
0.9%
7 65
 
0.6%
8 47
 
0.4%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 7590
28.2%
1 1199
 
4.5%
2 504
 
1.9%
3 290
 
1.1%
4 181
 
0.7%
5 123
 
0.5%
6 100
 
0.4%
7 65
 
0.2%
8 47
 
0.2%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 17558
152.4%
1 2798
 
24.3%
2 1210
 
10.5%
3 647
 
5.6%
4 410
 
3.6%
5 268
 
2.3%
6 219
 
1.9%
7 143
 
1.2%
8 146
 
1.3%

CC_enq_L6m
Real number (ℝ)

 TrainTest
Distinct1515
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.121721660.11761132
 TrainTest
Minimum-1-1
Maximum1713
Zeros207038914
Zeros (%)77.0%77.4%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:21.461522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q300
95-th percentile22
Maximum1713
Range1814
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.9362040.87309498
Coefficient of variation (CV)7.69135087.4235626
Kurtosis37.57128530.856881
Mean0.121721660.11761132
Median Absolute Deviation (MAD)00
Skewness4.71412414.1876251
Sum32721355
Variance0.876477930.76229485
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:21.539181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 20703
77.0%
-1 2986
 
11.1%
1 1822
 
6.8%
2 666
 
2.5%
3 332
 
1.2%
4 149
 
0.6%
6 68
 
0.3%
5 61
 
0.2%
7 44
 
0.2%
9 14
 
0.1%
Other values (5) 36
 
0.1%
ValueCountFrequency (%)
0 8914
77.4%
-1 1227
 
10.7%
1 793
 
6.9%
2 299
 
2.6%
3 139
 
1.2%
4 77
 
0.7%
5 26
 
0.2%
6 20
 
0.2%
7 13
 
0.1%
9 4
 
< 0.1%
Other values (5) 9
 
0.1%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 20703
77.0%
1 1822
 
6.8%
2 666
 
2.5%
3 332
 
1.2%
4 149
 
0.6%
5 61
 
0.2%
6 68
 
0.3%
7 44
 
0.2%
8 14
 
0.1%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 8914
77.4%
1 793
 
6.9%
2 299
 
2.6%
3 139
 
1.2%
4 77
 
0.7%
5 26
 
0.2%
6 20
 
0.2%
7 13
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 8914
33.2%
1 793
 
3.0%
2 299
 
1.1%
3 139
 
0.5%
4 77
 
0.3%
5 26
 
0.1%
6 20
 
0.1%
7 13
 
< 0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 20703
179.7%
1 1822
 
15.8%
2 666
 
5.8%
3 332
 
2.9%
4 149
 
1.3%
5 61
 
0.5%
6 68
 
0.6%
7 44
 
0.4%
8 14
 
0.1%

CC_enq_L12m
Real number (ℝ)

 TrainTest
Distinct2118
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.28741490.2761913
 TrainTest
Minimum-1-1
Maximum2416
Zeros194548393
Zeros (%)72.4%72.8%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:21.622904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q300
95-th percentile22
Maximum2416
Range2517
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation1.36788521.2830072
Coefficient of variation (CV)4.75927024.6453571
Kurtosis34.52677529.108085
Mean0.28741490.2761913
Median Absolute Deviation (MAD)00
Skewness4.80164514.4817263
Sum77263182
Variance1.87110981.6461076
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:21.704971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 19454
72.4%
-1 2986
 
11.1%
1 2253
 
8.4%
2 901
 
3.4%
3 447
 
1.7%
4 266
 
1.0%
5 150
 
0.6%
6 126
 
0.5%
7 89
 
0.3%
8 54
 
0.2%
Other values (11) 155
 
0.6%
ValueCountFrequency (%)
0 8393
72.8%
-1 1227
 
10.7%
1 968
 
8.4%
2 372
 
3.2%
3 214
 
1.9%
4 137
 
1.2%
5 64
 
0.6%
6 51
 
0.4%
7 26
 
0.2%
9 16
 
0.1%
Other values (8) 53
 
0.5%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 19454
72.4%
1 2253
 
8.4%
2 901
 
3.4%
3 447
 
1.7%
4 266
 
1.0%
5 150
 
0.6%
6 126
 
0.5%
7 89
 
0.3%
8 54
 
0.2%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 8393
72.8%
1 968
 
8.4%
2 372
 
3.2%
3 214
 
1.9%
4 137
 
1.2%
5 64
 
0.6%
6 51
 
0.4%
7 26
 
0.2%
8 15
 
0.1%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 8393
31.2%
1 968
 
3.6%
2 372
 
1.4%
3 214
 
0.8%
4 137
 
0.5%
5 64
 
0.2%
6 51
 
0.2%
7 26
 
0.1%
8 15
 
0.1%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 19454
168.9%
1 2253
 
19.6%
2 901
 
7.8%
3 447
 
3.9%
4 266
 
2.3%
5 150
 
1.3%
6 126
 
1.1%
7 89
 
0.8%
8 54
 
0.5%

PL_enq
Real number (ℝ)

 TrainTest
Distinct3934
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.12759941.0674421
 TrainTest
Minimum-1-1
Maximum4644
Zeros131445680
Zeros (%)48.9%49.3%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:21.797281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q311
95-th percentile66
Maximum4644
Range4745
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation2.76344342.5522763
Coefficient of variation (CV)2.45073152.3910209
Kurtosis36.45403736.67641
Mean1.12759941.0674421
Median Absolute Deviation (MAD)11
Skewness4.67463014.5343793
Sum3031112298
Variance7.63661966.5141144
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:21.899390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 13144
48.9%
1 4333
 
16.1%
-1 2986
 
11.1%
2 2327
 
8.7%
3 1262
 
4.7%
4 864
 
3.2%
5 458
 
1.7%
6 389
 
1.4%
7 261
 
1.0%
8 190
 
0.7%
Other values (29) 667
 
2.5%
ValueCountFrequency (%)
0 5680
49.3%
1 1895
 
16.4%
-1 1227
 
10.7%
2 1048
 
9.1%
3 542
 
4.7%
4 344
 
3.0%
5 196
 
1.7%
6 162
 
1.4%
7 106
 
0.9%
8 72
 
0.6%
Other values (24) 249
 
2.2%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 13144
48.9%
1 4333
 
16.1%
2 2327
 
8.7%
3 1262
 
4.7%
4 864
 
3.2%
5 458
 
1.7%
6 389
 
1.4%
7 261
 
1.0%
8 190
 
0.7%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 5680
49.3%
1 1895
 
16.4%
2 1048
 
9.1%
3 542
 
4.7%
4 344
 
3.0%
5 196
 
1.7%
6 162
 
1.4%
7 106
 
0.9%
8 72
 
0.6%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 5680
21.1%
1 1895
 
7.0%
2 1048
 
3.9%
3 542
 
2.0%
4 344
 
1.3%
5 196
 
0.7%
6 162
 
0.6%
7 106
 
0.4%
8 72
 
0.3%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 13144
114.1%
1 4333
 
37.6%
2 2327
 
20.2%
3 1262
 
11.0%
4 864
 
7.5%
5 458
 
4.0%
6 389
 
3.4%
7 261
 
2.3%
8 190
 
1.6%

PL_enq_L6m
Real number (ℝ)

 TrainTest
Distinct2723
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.403221610.39345543
 TrainTest
Minimum-1-1
Maximum2844
Zeros175597558
Zeros (%)65.3%65.6%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:21.990964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q300
95-th percentile33
Maximum2844
Range2945
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation1.50008041.4612312
Coefficient of variation (CV)3.72023813.7138419
Kurtosis52.420728105.28178
Mean0.403221610.39345543
Median Absolute Deviation (MAD)00
Skewness5.39933936.6551906
Sum108394533
Variance2.25024112.1351967
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:22.076673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 17559
65.3%
1 3430
 
12.8%
-1 2986
 
11.1%
2 1396
 
5.2%
3 605
 
2.3%
4 301
 
1.1%
5 171
 
0.6%
6 157
 
0.6%
7 72
 
0.3%
8 57
 
0.2%
Other values (17) 147
 
0.5%
ValueCountFrequency (%)
0 7558
65.6%
1 1495
 
13.0%
-1 1227
 
10.7%
2 634
 
5.5%
3 258
 
2.2%
4 109
 
0.9%
5 83
 
0.7%
6 51
 
0.4%
8 31
 
0.3%
7 27
 
0.2%
Other values (13) 48
 
0.4%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 17559
65.3%
1 3430
 
12.8%
2 1396
 
5.2%
3 605
 
2.3%
4 301
 
1.1%
5 171
 
0.6%
6 157
 
0.6%
7 72
 
0.3%
8 57
 
0.2%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 7558
65.6%
1 1495
 
13.0%
2 634
 
5.5%
3 258
 
2.2%
4 109
 
0.9%
5 83
 
0.7%
6 51
 
0.4%
7 27
 
0.2%
8 31
 
0.3%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 7558
28.1%
1 1495
 
5.6%
2 634
 
2.4%
3 258
 
1.0%
4 109
 
0.4%
5 83
 
0.3%
6 51
 
0.2%
7 27
 
0.1%
8 31
 
0.1%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 17559
152.4%
1 3430
 
29.8%
2 1396
 
12.1%
3 605
 
5.3%
4 301
 
2.6%
5 171
 
1.5%
6 157
 
1.4%
7 72
 
0.6%
8 57
 
0.5%

PL_enq_L12m
Real number (ℝ)

 TrainTest
Distinct3429
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.671887210.65437028
 TrainTest
Minimum-1-1
Maximum4444
Zeros153346640
Zeros (%)57.0%57.6%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:22.166804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q311
95-th percentile44
Maximum4444
Range4545
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation2.00254721.9268683
Coefficient of variation (CV)2.98048122.9446146
Kurtosis62.05305271.526093
Mean0.671887210.65437028
Median Absolute Deviation (MAD)00
Skewness5.75087765.9808549
Sum180617539
Variance4.01019513.7128214
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:22.263203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 15334
57.0%
1 4269
 
15.9%
-1 2986
 
11.1%
2 1830
 
6.8%
3 958
 
3.6%
4 483
 
1.8%
5 262
 
1.0%
6 213
 
0.8%
7 184
 
0.7%
8 81
 
0.3%
Other values (24) 281
 
1.0%
ValueCountFrequency (%)
0 6640
57.6%
1 1783
 
15.5%
-1 1227
 
10.7%
2 847
 
7.4%
3 433
 
3.8%
4 190
 
1.6%
5 113
 
1.0%
6 75
 
0.7%
7 67
 
0.6%
8 36
 
0.3%
Other values (19) 110
 
1.0%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 15334
57.0%
1 4269
 
15.9%
2 1830
 
6.8%
3 958
 
3.6%
4 483
 
1.8%
5 262
 
1.0%
6 213
 
0.8%
7 184
 
0.7%
8 81
 
0.3%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 6640
57.6%
1 1783
 
15.5%
2 847
 
7.4%
3 433
 
3.8%
4 190
 
1.6%
5 113
 
1.0%
6 75
 
0.7%
7 67
 
0.6%
8 36
 
0.3%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 6640
24.7%
1 1783
 
6.6%
2 847
 
3.2%
3 433
 
1.6%
4 190
 
0.7%
5 113
 
0.4%
6 75
 
0.3%
7 67
 
0.2%
8 36
 
0.1%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 15334
133.1%
1 4269
 
37.1%
2 1830
 
15.9%
3 958
 
8.3%
4 483
 
4.2%
5 262
 
2.3%
6 213
 
1.8%
7 184
 
1.6%
8 81
 
0.7%

time_since_recent_enq
Real number (ℝ)

 TrainTest
Distinct17411337
Distinct (%)6.5%11.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean216.26952220.33799
 TrainTest
Minimum-1-1
Maximum47684208
Zeros23799
Zeros (%)0.9%0.9%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:22.370852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q144
median4445
Q3232240
95-th percentile10531066
Maximum47684208
Range47694209
Interquartile range (IQR)228236

Descriptive statistics

 TrainTest
Standard deviation425.93794426.51977
Coefficient of variation (CV)1.96947741.9357523
Kurtosis20.3368518.429499
Mean216.26952220.33799
Median Absolute Deviation (MAD)4546
Skewness3.85814423.7053572
Sum58135412538514
Variance181423.13181919.11
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:22.490210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 2986
 
11.1%
1 1253
 
4.7%
2 1151
 
4.3%
3 942
 
3.5%
4 709
 
2.6%
5 557
 
2.1%
6 516
 
1.9%
7 403
 
1.5%
8 310
 
1.2%
9 302
 
1.1%
Other values (1731) 17752
66.0%
ValueCountFrequency (%)
-1 1227
 
10.7%
2 540
 
4.7%
1 500
 
4.3%
3 409
 
3.6%
4 297
 
2.6%
6 218
 
1.9%
5 212
 
1.8%
7 186
 
1.6%
8 147
 
1.3%
9 138
 
1.2%
Other values (1327) 7647
66.4%
ValueCountFrequency (%)
-1 2986
11.1%
0 237
 
0.9%
1 1253
4.7%
2 1151
 
4.3%
3 942
 
3.5%
4 709
 
2.6%
5 557
 
2.1%
6 516
 
1.9%
7 403
 
1.5%
8 310
 
1.2%
ValueCountFrequency (%)
-1 1227
10.7%
0 99
 
0.9%
1 500
4.3%
2 540
4.7%
3 409
 
3.6%
4 297
 
2.6%
5 212
 
1.8%
6 218
 
1.9%
7 186
 
1.6%
8 147
 
1.3%
ValueCountFrequency (%)
-1 1227
4.6%
0 99
 
0.4%
1 500
1.9%
2 540
2.0%
3 409
 
1.5%
4 297
 
1.1%
5 212
 
0.8%
6 218
 
0.8%
7 186
 
0.7%
8 147
 
0.5%
ValueCountFrequency (%)
-1 2986
25.9%
0 237
 
2.1%
1 1253
10.9%
2 1151
 
10.0%
3 942
 
8.2%
4 709
 
6.2%
5 557
 
4.8%
6 516
 
4.5%
7 403
 
3.5%
8 310
 
2.7%

enq_L12m
Real number (ℝ)

 TrainTest
Distinct6154
Distinct (%)0.2%0.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.96744912.8831699
 TrainTest
Minimum-1-1
Maximum8787
Zeros46432064
Zeros (%)17.3%17.9%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:22.606901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median22
Q344
95-th percentile1211
Maximum8787
Range8888
Interquartile range (IQR)44

Descriptive statistics

 TrainTest
Standard deviation4.89875364.6785818
Coefficient of variation (CV)1.65082991.6227215
Kurtosis34.74010834.489202
Mean2.96744912.8831699
Median Absolute Deviation (MAD)22
Skewness4.29048694.2370123
Sum7976833217
Variance23.99778721.889127
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:22.928098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5664
21.1%
0 4643
17.3%
2 3682
13.7%
-1 2986
11.1%
3 2560
9.5%
4 1788
 
6.7%
5 1222
 
4.5%
6 856
 
3.2%
7 670
 
2.5%
8 510
 
1.9%
Other values (51) 2300
8.6%
ValueCountFrequency (%)
1 2404
20.9%
0 2064
17.9%
2 1570
13.6%
-1 1227
10.7%
3 1136
9.9%
4 828
 
7.2%
5 523
 
4.5%
6 374
 
3.2%
7 247
 
2.1%
8 213
 
1.8%
Other values (44) 935
 
8.1%
ValueCountFrequency (%)
-1 2986
11.1%
0 4643
17.3%
1 5664
21.1%
2 3682
13.7%
3 2560
9.5%
4 1788
 
6.7%
5 1222
 
4.5%
6 856
 
3.2%
7 670
 
2.5%
8 510
 
1.9%
ValueCountFrequency (%)
-1 1227
10.7%
0 2064
17.9%
1 2404
20.9%
2 1570
13.6%
3 1136
9.9%
4 828
 
7.2%
5 523
 
4.5%
6 374
 
3.2%
7 247
 
2.1%
8 213
 
1.8%
ValueCountFrequency (%)
-1 1227
4.6%
0 2064
7.7%
1 2404
8.9%
2 1570
5.8%
3 1136
4.2%
4 828
 
3.1%
5 523
 
1.9%
6 374
 
1.4%
7 247
 
0.9%
8 213
 
0.8%
ValueCountFrequency (%)
-1 2986
25.9%
0 4643
40.3%
1 5664
49.2%
2 3682
32.0%
3 2560
22.2%
4 1788
 
15.5%
5 1222
 
10.6%
6 856
 
7.4%
7 670
 
5.8%
8 510
 
4.4%

enq_L6m
Real number (ℝ)

 TrainTest
Distinct4944
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.89736241.8398577
 TrainTest
Minimum-1-1
Maximum6666
Zeros78853426
Zeros (%)29.3%29.7%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:23.038728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median11
Q332
95-th percentile87
Maximum6666
Range6767
Interquartile range (IQR)32

Descriptive statistics

 TrainTest
Standard deviation3.52954063.3587771
Coefficient of variation (CV)1.86023531.8255636
Kurtosis37.60584843.418946
Mean1.89736241.8398577
Median Absolute Deviation (MAD)11
Skewness4.46823054.6362058
Sum5100321197
Variance12.45765711.281383
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:23.151867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 7885
29.3%
1 5813
21.6%
2 3367
12.5%
-1 2986
 
11.1%
3 2174
 
8.1%
4 1311
 
4.9%
5 852
 
3.2%
6 612
 
2.3%
7 426
 
1.6%
8 298
 
1.1%
Other values (39) 1157
 
4.3%
ValueCountFrequency (%)
0 3426
29.7%
1 2491
21.6%
2 1500
13.0%
-1 1227
 
10.7%
3 920
 
8.0%
4 583
 
5.1%
5 354
 
3.1%
6 258
 
2.2%
7 188
 
1.6%
8 116
 
1.0%
Other values (34) 458
 
4.0%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 7885
29.3%
1 5813
21.6%
2 3367
12.5%
3 2174
 
8.1%
4 1311
 
4.9%
5 852
 
3.2%
6 612
 
2.3%
7 426
 
1.6%
8 298
 
1.1%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 3426
29.7%
1 2491
21.6%
2 1500
13.0%
3 920
 
8.0%
4 583
 
5.1%
5 354
 
3.1%
6 258
 
2.2%
7 188
 
1.6%
8 116
 
1.0%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 3426
12.7%
1 2491
9.3%
2 1500
5.6%
3 920
 
3.4%
4 583
 
2.2%
5 354
 
1.3%
6 258
 
1.0%
7 188
 
0.7%
8 116
 
0.4%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 7885
68.4%
1 5813
50.5%
2 3367
29.2%
3 2174
 
18.9%
4 1311
 
11.4%
5 852
 
7.4%
6 612
 
5.3%
7 426
 
3.7%
8 298
 
2.6%

enq_L3m
Real number (ℝ)

 TrainTest
Distinct3528
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.11279341.0825449
 TrainTest
Minimum-1-1
Maximum4235
Zeros107564675
Zeros (%)40.0%40.6%
Negative29861227
Negative (%)11.1%10.7%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:23.257477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q100
median00
Q322
95-th percentile55
Maximum4235
Range4336
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation2.32991362.171479
Coefficient of variation (CV)2.09375212.005902
Kurtosis39.25423632.16449
Mean1.11279341.0825449
Median Absolute Deviation (MAD)11
Skewness4.55198364.0238561
Sum2991312472
Variance5.42849744.7153212
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:23.353231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 10756
40.0%
1 6049
22.5%
2 2992
 
11.1%
-1 2986
 
11.1%
3 1654
 
6.2%
4 921
 
3.4%
5 448
 
1.7%
6 311
 
1.2%
7 213
 
0.8%
8 125
 
0.5%
Other values (25) 426
 
1.6%
ValueCountFrequency (%)
0 4675
40.6%
1 2592
22.5%
2 1302
 
11.3%
-1 1227
 
10.7%
3 702
 
6.1%
4 394
 
3.4%
5 186
 
1.6%
6 142
 
1.2%
7 73
 
0.6%
8 65
 
0.6%
Other values (18) 163
 
1.4%
ValueCountFrequency (%)
-1 2986
 
11.1%
0 10756
40.0%
1 6049
22.5%
2 2992
 
11.1%
3 1654
 
6.2%
4 921
 
3.4%
5 448
 
1.7%
6 311
 
1.2%
7 213
 
0.8%
8 125
 
0.5%
ValueCountFrequency (%)
-1 1227
 
10.7%
0 4675
40.6%
1 2592
22.5%
2 1302
 
11.3%
3 702
 
6.1%
4 394
 
3.4%
5 186
 
1.6%
6 142
 
1.2%
7 73
 
0.6%
8 65
 
0.6%
ValueCountFrequency (%)
-1 1227
 
4.6%
0 4675
17.4%
1 2592
9.6%
2 1302
 
4.8%
3 702
 
2.6%
4 394
 
1.5%
5 186
 
0.7%
6 142
 
0.5%
7 73
 
0.3%
8 65
 
0.2%
ValueCountFrequency (%)
-1 2986
 
25.9%
0 10756
93.4%
1 6049
52.5%
2 2992
 
26.0%
3 1654
 
14.4%
4 921
 
8.0%
5 448
 
3.9%
6 311
 
2.7%
7 213
 
1.8%
8 125
 
1.1%

MARITALSTATUS
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
Married
19835 
Single
7046 
Married
8378 
Single
3143 

Length

 TrainTest
Max length77
Median length77
Mean length6.73788186.7271938
Min length66

Characters and Unicode

 TrainTest
Total characters18112177504
Distinct characters1010
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowMarriedMarried
2nd rowMarriedSingle
3rd rowMarriedMarried
4th rowSingleMarried
5th rowMarriedMarried

Common Values

ValueCountFrequency (%)
Married 19835
73.8%
Single 7046
 
26.2%
ValueCountFrequency (%)
Married 8378
72.7%
Single 3143
 
27.3%

Length

2025-03-10T23:50:23.438830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:23.503910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:23.557507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
married 19835
73.8%
single 7046
 
26.2%
ValueCountFrequency (%)
married 8378
72.7%
single 3143
 
27.3%

Most occurring characters

ValueCountFrequency (%)
r 39670
21.9%
i 26881
14.8%
e 26881
14.8%
M 19835
11.0%
a 19835
11.0%
d 19835
11.0%
S 7046
 
3.9%
n 7046
 
3.9%
g 7046
 
3.9%
l 7046
 
3.9%
ValueCountFrequency (%)
r 16756
21.6%
i 11521
14.9%
e 11521
14.9%
M 8378
10.8%
a 8378
10.8%
d 8378
10.8%
S 3143
 
4.1%
n 3143
 
4.1%
g 3143
 
4.1%
l 3143
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 181121
100.0%
ValueCountFrequency (%)
(unknown) 77504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 39670
21.9%
i 26881
14.8%
e 26881
14.8%
M 19835
11.0%
a 19835
11.0%
d 19835
11.0%
S 7046
 
3.9%
n 7046
 
3.9%
g 7046
 
3.9%
l 7046
 
3.9%
ValueCountFrequency (%)
r 16756
21.6%
i 11521
14.9%
e 11521
14.9%
M 8378
10.8%
a 8378
10.8%
d 8378
10.8%
S 3143
 
4.1%
n 3143
 
4.1%
g 3143
 
4.1%
l 3143
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 181121
100.0%
ValueCountFrequency (%)
(unknown) 77504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 39670
21.9%
i 26881
14.8%
e 26881
14.8%
M 19835
11.0%
a 19835
11.0%
d 19835
11.0%
S 7046
 
3.9%
n 7046
 
3.9%
g 7046
 
3.9%
l 7046
 
3.9%
ValueCountFrequency (%)
r 16756
21.6%
i 11521
14.9%
e 11521
14.9%
M 8378
10.8%
a 8378
10.8%
d 8378
10.8%
S 3143
 
4.1%
n 3143
 
4.1%
g 3143
 
4.1%
l 3143
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 181121
100.0%
ValueCountFrequency (%)
(unknown) 77504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 39670
21.9%
i 26881
14.8%
e 26881
14.8%
M 19835
11.0%
a 19835
11.0%
d 19835
11.0%
S 7046
 
3.9%
n 7046
 
3.9%
g 7046
 
3.9%
l 7046
 
3.9%
ValueCountFrequency (%)
r 16756
21.6%
i 11521
14.9%
e 11521
14.9%
M 8378
10.8%
a 8378
10.8%
d 8378
10.8%
S 3143
 
4.1%
n 3143
 
4.1%
g 3143
 
4.1%
l 3143
 
4.1%

EDUCATION
Categorical

 TrainTest
Distinct77
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
GRADUATE
9106 
12TH
7431 
SSC
4574 
UNDER GRADUATE
2844 
OTHERS
1453 
Other values (2)
1473 
GRADUATE
3950 
12TH
3111 
SSC
1976 
UNDER GRADUATE
1258 
OTHERS
628 
Other values (2)
598 

Length

 TrainTest
Max length1414
Median length1313
Mean length6.83888256.8624251
Min length33

Characters and Unicode

 TrainTest
Total characters18383679062
Distinct characters2020
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowSSCOTHERS
2nd rowGRADUATE12TH
3rd rowSSCGRADUATE
4th rowGRADUATESSC
5th rowOTHERSGRADUATE

Common Values

ValueCountFrequency (%)
GRADUATE 9106
33.9%
12TH 7431
27.6%
SSC 4574
17.0%
UNDER GRADUATE 2844
 
10.6%
OTHERS 1453
 
5.4%
POST-GRADUATE 1332
 
5.0%
PROFESSIONAL 141
 
0.5%
ValueCountFrequency (%)
GRADUATE 3950
34.3%
12TH 3111
27.0%
SSC 1976
17.2%
UNDER GRADUATE 1258
 
10.9%
OTHERS 628
 
5.5%
POST-GRADUATE 534
 
4.6%
PROFESSIONAL 64
 
0.6%

Length

2025-03-10T23:50:23.629633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:23.708215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:23.791842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
graduate 11950
40.2%
12th 7431
25.0%
ssc 4574
 
15.4%
under 2844
 
9.6%
others 1453
 
4.9%
post-graduate 1332
 
4.5%
professional 141
 
0.5%
ValueCountFrequency (%)
graduate 5208
40.8%
12th 3111
24.3%
ssc 1976
 
15.5%
under 1258
 
9.8%
others 628
 
4.9%
post-graduate 534
 
4.2%
professional 64
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 26705
14.5%
T 23498
12.8%
R 17720
9.6%
E 17720
9.6%
D 16126
8.8%
U 16126
8.8%
G 13282
7.2%
S 12215
6.6%
H 8884
 
4.8%
2 7431
 
4.0%
Other values (10) 24129
13.1%
ValueCountFrequency (%)
A 11548
14.6%
T 10015
12.7%
R 7692
9.7%
E 7692
9.7%
D 7000
8.9%
U 7000
8.9%
G 5742
7.3%
S 5242
6.6%
H 3739
 
4.7%
2 3111
 
3.9%
Other values (10) 10281
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183836
100.0%
ValueCountFrequency (%)
(unknown) 79062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 26705
14.5%
T 23498
12.8%
R 17720
9.6%
E 17720
9.6%
D 16126
8.8%
U 16126
8.8%
G 13282
7.2%
S 12215
6.6%
H 8884
 
4.8%
2 7431
 
4.0%
Other values (10) 24129
13.1%
ValueCountFrequency (%)
A 11548
14.6%
T 10015
12.7%
R 7692
9.7%
E 7692
9.7%
D 7000
8.9%
U 7000
8.9%
G 5742
7.3%
S 5242
6.6%
H 3739
 
4.7%
2 3111
 
3.9%
Other values (10) 10281
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183836
100.0%
ValueCountFrequency (%)
(unknown) 79062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 26705
14.5%
T 23498
12.8%
R 17720
9.6%
E 17720
9.6%
D 16126
8.8%
U 16126
8.8%
G 13282
7.2%
S 12215
6.6%
H 8884
 
4.8%
2 7431
 
4.0%
Other values (10) 24129
13.1%
ValueCountFrequency (%)
A 11548
14.6%
T 10015
12.7%
R 7692
9.7%
E 7692
9.7%
D 7000
8.9%
U 7000
8.9%
G 5742
7.3%
S 5242
6.6%
H 3739
 
4.7%
2 3111
 
3.9%
Other values (10) 10281
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183836
100.0%
ValueCountFrequency (%)
(unknown) 79062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 26705
14.5%
T 23498
12.8%
R 17720
9.6%
E 17720
9.6%
D 16126
8.8%
U 16126
8.8%
G 13282
7.2%
S 12215
6.6%
H 8884
 
4.8%
2 7431
 
4.0%
Other values (10) 24129
13.1%
ValueCountFrequency (%)
A 11548
14.6%
T 10015
12.7%
R 7692
9.7%
E 7692
9.7%
D 7000
8.9%
U 7000
8.9%
G 5742
7.3%
S 5242
6.6%
H 3739
 
4.7%
2 3111
 
3.9%
Other values (10) 10281
13.0%

AGE
Real number (ℝ)

 TrainTest
Distinct4645
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean33.75901233.65654
 TrainTest
Minimum2121
Maximum7765
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:23.894075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum2121
5-th percentile2222
Q12727
median3232
Q33939
95-th percentile5151
Maximum7765
Range5644
Interquartile range (IQR)1212

Descriptive statistics

 TrainTest
Standard deviation8.7465198.7561582
Coefficient of variation (CV)0.259086940.26016216
Kurtosis0.0387391910.066933517
Mean33.75901233.65654
Median Absolute Deviation (MAD)66
Skewness0.777989370.79024104
Sum907476387757
Variance76.50159576.670306
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:24.002247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
27 1373
 
5.1%
29 1349
 
5.0%
28 1326
 
4.9%
30 1323
 
4.9%
25 1293
 
4.8%
26 1252
 
4.7%
31 1215
 
4.5%
32 1203
 
4.5%
24 1186
 
4.4%
33 1180
 
4.4%
Other values (36) 14181
52.8%
ValueCountFrequency (%)
29 615
 
5.3%
27 583
 
5.1%
28 558
 
4.8%
30 547
 
4.7%
26 545
 
4.7%
24 544
 
4.7%
31 537
 
4.7%
32 527
 
4.6%
25 518
 
4.5%
33 473
 
4.1%
Other values (35) 6074
52.7%
ValueCountFrequency (%)
21 554
2.1%
22 838
3.1%
23 1026
3.8%
24 1186
4.4%
25 1293
4.8%
26 1252
4.7%
27 1373
5.1%
28 1326
4.9%
29 1349
5.0%
30 1323
4.9%
ValueCountFrequency (%)
21 242
 
2.1%
22 376
3.3%
23 472
4.1%
24 544
4.7%
25 518
4.5%
26 545
4.7%
27 583
5.1%
28 558
4.8%
29 615
5.3%
30 547
4.7%
ValueCountFrequency (%)
21 242
 
0.9%
22 376
1.4%
23 472
1.8%
24 544
2.0%
25 518
1.9%
26 545
2.0%
27 583
2.2%
28 558
2.1%
29 615
2.3%
30 547
2.0%
ValueCountFrequency (%)
21 554
4.8%
22 838
7.3%
23 1026
8.9%
24 1186
10.3%
25 1293
11.2%
26 1252
10.9%
27 1373
11.9%
28 1326
11.5%
29 1349
11.7%
30 1323
11.5%

GENDER
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
M
23875 
F
3006 
M
10191 
F
1330 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowMF
2nd rowFM
3rd rowMM
4th rowMM
5th rowFM

Common Values

ValueCountFrequency (%)
M 23875
88.8%
F 3006
 
11.2%
ValueCountFrequency (%)
M 10191
88.5%
F 1330
 
11.5%

Length

2025-03-10T23:50:24.094422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:24.154974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:24.208523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
m 23875
88.8%
f 3006
 
11.2%
ValueCountFrequency (%)
m 10191
88.5%
f 1330
 
11.5%

Most occurring characters

ValueCountFrequency (%)
M 23875
88.8%
F 3006
 
11.2%
ValueCountFrequency (%)
M 10191
88.5%
F 1330
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 23875
88.8%
F 3006
 
11.2%
ValueCountFrequency (%)
M 10191
88.5%
F 1330
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 23875
88.8%
F 3006
 
11.2%
ValueCountFrequency (%)
M 10191
88.5%
F 1330
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 23875
88.8%
F 3006
 
11.2%
ValueCountFrequency (%)
M 10191
88.5%
F 1330
 
11.5%

NETMONTHLYINCOME
Real number (ℝ)

 TrainTest
Distinct738479
Distinct (%)2.7%4.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean27175.48227298.744
 TrainTest
Minimum00
Maximum7000002500000
Zeros1711
Zeros (%)0.1%0.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:24.301669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile1200012000
Q11800018000
median2400024000
Q33200032000
95-th percentile5000050000
Maximum7000002500000
Range7000002500000
Interquartile range (IQR)1400014000

Descriptive statistics

 TrainTest
Standard deviation19599.82529141.73
Coefficient of variation (CV)0.721231911.0675117
Kurtosis280.505114524.4074
Mean27175.48227298.744
Median Absolute Deviation (MAD)60006000
Skewness11.78931855.070414
Sum7.3050412 × 1083.1450883 × 108
Variance3.8415312 × 1088.4924044 × 108
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:24.426281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 2496
 
9.3%
25000 2427
 
9.0%
30000 2105
 
7.8%
15000 1871
 
7.0%
18000 1538
 
5.7%
35000 1319
 
4.9%
40000 1051
 
3.9%
22000 785
 
2.9%
45000 624
 
2.3%
28000 615
 
2.3%
Other values (728) 12050
44.8%
ValueCountFrequency (%)
20000 1142
 
9.9%
25000 1062
 
9.2%
30000 876
 
7.6%
15000 790
 
6.9%
18000 631
 
5.5%
35000 575
 
5.0%
40000 449
 
3.9%
22000 311
 
2.7%
45000 274
 
2.4%
28000 261
 
2.3%
Other values (469) 5150
44.7%
ValueCountFrequency (%)
0 17
0.1%
1 4
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
10 36
0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
0 11
0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
10 10
0.1%
12 6
0.1%
15 4
 
< 0.1%
18 1
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
0 11
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
10 10
< 0.1%
12 6
< 0.1%
15 4
 
< 0.1%
18 1
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
0 17
0.1%
1 4
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
10 36
0.3%
11 4
 
< 0.1%

Time_With_Curr_Empr
Real number (ℝ)

 TrainTest
Distinct426379
Distinct (%)1.6%3.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean109.91005110.00373
 TrainTest
Minimum03
Maximum1020839
Zeros10
Zeros (%)< 0.1%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:24.548058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum03
5-th percentile2727
Q16160
median9090
Q3131131
95-th percentile250251
Maximum1020839
Range1020836
Interquartile range (IQR)7071

Descriptive statistics

 TrainTest
Standard deviation75.77186576.92445
Coefficient of variation (CV)0.689398890.69928946
Kurtosis6.76916525.912624
Mean109.91005110.00373
Median Absolute Deviation (MAD)3737
Skewness1.86758461.8571038
Sum29544921267353
Variance5741.37555917.3711
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:24.666734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 866
 
3.2%
125 789
 
2.9%
126 662
 
2.5%
71 494
 
1.8%
62 453
 
1.7%
65 445
 
1.7%
102 381
 
1.4%
30 379
 
1.4%
130 370
 
1.4%
77 368
 
1.4%
Other values (416) 21674
80.6%
ValueCountFrequency (%)
66 369
 
3.2%
125 344
 
3.0%
126 279
 
2.4%
71 193
 
1.7%
65 181
 
1.6%
62 176
 
1.5%
77 174
 
1.5%
63 170
 
1.5%
42 163
 
1.4%
130 162
 
1.4%
Other values (369) 9310
80.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 3
 
< 0.1%
5 6
 
< 0.1%
6 6
 
< 0.1%
9 1
 
< 0.1%
12 38
 
0.1%
13 73
0.3%
14 96
0.4%
15 52
0.2%
16 15
 
0.1%
ValueCountFrequency (%)
3 2
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
12 22
0.2%
13 33
0.3%
14 45
0.4%
15 26
0.2%
ValueCountFrequency (%)
3 2
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
12 22
0.1%
13 33
0.1%
14 45
0.2%
15 26
0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 3
 
< 0.1%
5 6
 
0.1%
6 6
 
0.1%
9 1
 
< 0.1%
12 38
 
0.3%
13 73
0.6%
14 96
0.8%
15 52
0.5%
16 15
 
0.1%

pct_of_active_TLs_ever
Real number (ℝ)

 TrainTest
Distinct332265
Distinct (%)1.2%2.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.58464190.58885383
 TrainTest
Minimum00
Maximum11
Zeros40771682
Zeros (%)15.2%14.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:24.787540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q10.2860.286
median0.5950.6
Q311
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.7140.714

Descriptive statistics

 TrainTest
Standard deviation0.372464410.369496
Coefficient of variation (CV)0.637081270.62748339
Kurtosis-1.3575591-1.3313392
Mean0.58464190.58885383
Median Absolute Deviation (MAD)0.4050.4
Skewness-0.24785968-0.26136069
Sum15715.7596784.185
Variance0.138729730.13652729
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:24.907259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9440
35.1%
0 4077
15.2%
0.5 2915
 
10.8%
0.667 1440
 
5.4%
0.333 1229
 
4.6%
0.75 676
 
2.5%
0.25 602
 
2.2%
0.6 430
 
1.6%
0.4 430
 
1.6%
0.2 348
 
1.3%
Other values (322) 5294
19.7%
ValueCountFrequency (%)
1 4047
35.1%
0 1682
14.6%
0.5 1318
 
11.4%
0.667 601
 
5.2%
0.333 503
 
4.4%
0.25 279
 
2.4%
0.75 278
 
2.4%
0.6 223
 
1.9%
0.4 198
 
1.7%
0.8 151
 
1.3%
Other values (255) 2241
19.5%
ValueCountFrequency (%)
0 4077
15.2%
0.016 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 2
 
< 0.1%
0.022 1
 
< 0.1%
0.025 2
 
< 0.1%
0.026 1
 
< 0.1%
0.027 1
 
< 0.1%
0.029 3
 
< 0.1%
0.031 4
 
< 0.1%
ValueCountFrequency (%)
0 1682
14.6%
0.016 1
 
< 0.1%
0.017 1
 
< 0.1%
0.019 1
 
< 0.1%
0.02 1
 
< 0.1%
0.021 1
 
< 0.1%
0.024 1
 
< 0.1%
0.025 1
 
< 0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
ValueCountFrequency (%)
0 1682
6.3%
0.016 1
 
< 0.1%
0.017 1
 
< 0.1%
0.019 1
 
< 0.1%
0.02 1
 
< 0.1%
0.021 1
 
< 0.1%
0.024 1
 
< 0.1%
0.025 1
 
< 0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
ValueCountFrequency (%)
0 4077
35.4%
0.016 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 2
 
< 0.1%
0.022 1
 
< 0.1%
0.025 2
 
< 0.1%
0.026 1
 
< 0.1%
0.027 1
 
< 0.1%
0.029 3
 
< 0.1%
0.031 4
 
< 0.1%
 TrainTest
Distinct8668
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.312500280.30968796
 TrainTest
Minimum00
Maximum11
Zeros154216654
Zeros (%)57.4%57.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:25.027843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.6670.667
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.6670.667

Descriptive statistics

 TrainTest
Standard deviation0.403800480.40315285
Coefficient of variation (CV)1.29216041.3018034
Kurtosis-1.0729818-1.0492012
Mean0.312500280.30968796
Median Absolute Deviation (MAD)00
Skewness0.777361120.79264279
Sum8400.323567.915
Variance0.163054830.16253222
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:25.150918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15421
57.4%
1 5260
 
19.6%
0.5 2375
 
8.8%
0.667 833
 
3.1%
0.333 743
 
2.8%
0.25 323
 
1.2%
0.75 316
 
1.2%
0.6 229
 
0.9%
0.4 211
 
0.8%
0.8 134
 
0.5%
Other values (76) 1036
 
3.9%
ValueCountFrequency (%)
0 6654
57.8%
1 2238
 
19.4%
0.5 1002
 
8.7%
0.667 338
 
2.9%
0.333 328
 
2.8%
0.75 140
 
1.2%
0.25 131
 
1.1%
0.4 103
 
0.9%
0.6 88
 
0.8%
0.8 64
 
0.6%
Other values (58) 435
 
3.8%
ValueCountFrequency (%)
0 15421
57.4%
0.071 2
 
< 0.1%
0.111 1
 
< 0.1%
0.118 1
 
< 0.1%
0.125 9
 
< 0.1%
0.133 1
 
< 0.1%
0.143 45
 
0.2%
0.154 4
 
< 0.1%
0.167 45
 
0.2%
0.182 3
 
< 0.1%
ValueCountFrequency (%)
0 6654
57.8%
0.125 7
 
0.1%
0.143 13
 
0.1%
0.154 2
 
< 0.1%
0.167 19
 
0.2%
0.182 3
 
< 0.1%
0.2 47
 
0.4%
0.214 2
 
< 0.1%
0.222 4
 
< 0.1%
0.25 131
 
1.1%
ValueCountFrequency (%)
0 6654
24.8%
0.125 7
 
< 0.1%
0.143 13
 
< 0.1%
0.154 2
 
< 0.1%
0.167 19
 
0.1%
0.182 3
 
< 0.1%
0.2 47
 
0.2%
0.214 2
 
< 0.1%
0.222 4
 
< 0.1%
0.25 131
 
0.5%
ValueCountFrequency (%)
0 15421
133.9%
0.071 2
 
< 0.1%
0.111 1
 
< 0.1%
0.118 1
 
< 0.1%
0.125 9
 
0.1%
0.133 1
 
< 0.1%
0.143 45
 
0.4%
0.154 4
 
< 0.1%
0.167 45
 
0.4%
0.182 3
 
< 0.1%

pct_currentBal_all_TL
Real number (ℝ)

 TrainTest
Distinct13691240
Distinct (%)5.1%10.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.943905360.56563831
 TrainTest
Minimum-1-1
Maximum6327.523.154
Zeros56152362
Zeros (%)20.9%20.5%
Negative3015
Negative (%)0.1%0.1%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:25.265844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile00
Q10.1660.171
median0.6380.646
Q30.890.892
95-th percentile1.0271.027
Maximum6327.523.154
Range6328.524.154
Interquartile range (IQR)0.7240.721

Descriptive statistics

 TrainTest
Standard deviation41.0753750.50575031
Coefficient of variation (CV)43.5164120.89412315
Kurtosis21107.288710.24058
Mean0.943905360.56563831
Median Absolute Deviation (MAD)0.3050.302
Skewness140.3252116.496117
Sum25373.126516.719
Variance1687.18640.25578337
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:25.386572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5615
 
20.9%
1 1330
 
4.9%
0.5 189
 
0.7%
0.333 116
 
0.4%
0.667 112
 
0.4%
0.7 103
 
0.4%
0.6 92
 
0.3%
0.4 91
 
0.3%
0.3 85
 
0.3%
1.002 72
 
0.3%
Other values (1359) 19076
71.0%
ValueCountFrequency (%)
0 2362
 
20.5%
1 580
 
5.0%
0.5 92
 
0.8%
0.3 51
 
0.4%
0.333 44
 
0.4%
0.6 44
 
0.4%
0.667 43
 
0.4%
0.833 41
 
0.4%
0.417 37
 
0.3%
0.2 36
 
0.3%
Other values (1230) 8191
71.1%
ValueCountFrequency (%)
-1 30
 
0.1%
0 5615
20.9%
0.001 25
 
0.1%
0.002 10
 
< 0.1%
0.003 7
 
< 0.1%
0.004 5
 
< 0.1%
0.005 4
 
< 0.1%
0.006 8
 
< 0.1%
0.007 7
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
-1 15
 
0.1%
0 2362
20.5%
0.001 5
 
< 0.1%
0.002 7
 
0.1%
0.003 2
 
< 0.1%
0.004 1
 
< 0.1%
0.005 2
 
< 0.1%
0.006 2
 
< 0.1%
0.007 4
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
-1 15
 
0.1%
0 2362
8.8%
0.001 5
 
< 0.1%
0.002 7
 
< 0.1%
0.003 2
 
< 0.1%
0.004 1
 
< 0.1%
0.005 2
 
< 0.1%
0.006 2
 
< 0.1%
0.007 4
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
-1 30
 
0.3%
0 5615
48.7%
0.001 25
 
0.2%
0.002 10
 
0.1%
0.003 7
 
0.1%
0.004 5
 
< 0.1%
0.005 4
 
< 0.1%
0.006 8
 
0.1%
0.007 7
 
0.1%
0.008 3
 
< 0.1%

CC_utilization
Real number (ℝ)

 TrainTest
Distinct792618
Distinct (%)2.9%5.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-0.71515104-0.7154648
 TrainTest
Minimum-1-1
Maximum11
Zeros267119
Zeros (%)1.0%1.0%
Negative222879551
Negative (%)82.9%82.9%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:25.506299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q1-1-1
median-1-1
Q3-1-1
95-th percentile0.9580.953
Maximum11
Range22
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.643829890.64299503
Coefficient of variation (CV)-0.90027122-0.89870952
Kurtosis1.90841551.9073898
Mean-0.71515104-0.7154648
Median Absolute Deviation (MAD)00
Skewness1.92222961.9219475
Sum-19223.975-8242.87
Variance0.414516930.41344261
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:25.629248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 22287
82.9%
1 932
 
3.5%
0 267
 
1.0%
0.001 32
 
0.1%
0.913 28
 
0.1%
0.991 25
 
0.1%
0.999 22
 
0.1%
0.962 18
 
0.1%
0.967 17
 
0.1%
0.958 17
 
0.1%
Other values (782) 3236
 
12.0%
ValueCountFrequency (%)
-1 9551
82.9%
1 383
 
3.3%
0 119
 
1.0%
0.958 12
 
0.1%
0.948 12
 
0.1%
0.967 12
 
0.1%
0.991 10
 
0.1%
0.937 10
 
0.1%
0.004 9
 
0.1%
0.931 8
 
0.1%
Other values (608) 1395
 
12.1%
ValueCountFrequency (%)
-1 22287
82.9%
0 267
 
1.0%
0.001 32
 
0.1%
0.002 13
 
< 0.1%
0.003 3
 
< 0.1%
0.004 7
 
< 0.1%
0.005 2
 
< 0.1%
0.006 13
 
< 0.1%
0.007 8
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
-1 9551
82.9%
0 119
 
1.0%
0.001 7
 
0.1%
0.002 5
 
< 0.1%
0.003 1
 
< 0.1%
0.004 9
 
0.1%
0.005 3
 
< 0.1%
0.006 6
 
0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
ValueCountFrequency (%)
-1 9551
35.5%
0 119
 
0.4%
0.001 7
 
< 0.1%
0.002 5
 
< 0.1%
0.003 1
 
< 0.1%
0.004 9
 
< 0.1%
0.005 3
 
< 0.1%
0.006 6
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
ValueCountFrequency (%)
-1 22287
193.4%
0 267
 
2.3%
0.001 32
 
0.3%
0.002 13
 
0.1%
0.003 3
 
< 0.1%
0.004 7
 
0.1%
0.005 2
 
< 0.1%
0.006 13
 
0.1%
0.007 8
 
0.1%
0.008 3
 
< 0.1%

CC_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
21879 
1
5002 
0
9375 
1
2146 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 21879
81.4%
1 5002
 
18.6%
ValueCountFrequency (%)
0 9375
81.4%
1 2146
 
18.6%

Length

2025-03-10T23:50:25.726505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:25.786209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:25.839881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 21879
81.4%
1 5002
 
18.6%
ValueCountFrequency (%)
0 9375
81.4%
1 2146
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 21879
81.4%
1 5002
 
18.6%
ValueCountFrequency (%)
0 9375
81.4%
1 2146
 
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21879
81.4%
1 5002
 
18.6%
ValueCountFrequency (%)
0 9375
81.4%
1 2146
 
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21879
81.4%
1 5002
 
18.6%
ValueCountFrequency (%)
0 9375
81.4%
1 2146
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21879
81.4%
1 5002
 
18.6%
ValueCountFrequency (%)
0 9375
81.4%
1 2146
 
18.6%

PL_utilization
Real number (ℝ)

 TrainTest
Distinct743571
Distinct (%)2.8%5.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-0.72227041-0.7292906
 TrainTest
Minimum-1-1
Maximum1.3941.394
Zeros10746
Zeros (%)0.4%0.4%
Negative226269740
Negative (%)84.2%84.5%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:25.929828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q1-1-1
median-1-1
Q3-1-1
95-th percentile0.9190.909
Maximum1.3941.394
Range2.3942.394
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.648023290.6406305
Coefficient of variation (CV)-0.89720315-0.87842967
Kurtosis1.92736092.0899631
Mean-0.72227041-0.7292906
Median Absolute Deviation (MAD)00
Skewness1.95425841.9944776
Sum-19415.351-8402.157
Variance0.419934180.41040744
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:26.053200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 22626
84.2%
1 407
 
1.5%
0 107
 
0.4%
0.949 39
 
0.1%
0.967 29
 
0.1%
0.842 28
 
0.1%
0.894 24
 
0.1%
0.931 24
 
0.1%
0.965 22
 
0.1%
0.809 22
 
0.1%
Other values (733) 3553
 
13.2%
ValueCountFrequency (%)
-1 9740
84.5%
1 149
 
1.3%
0 46
 
0.4%
0.984 17
 
0.1%
0.843 13
 
0.1%
0.931 13
 
0.1%
0.97 12
 
0.1%
0.842 11
 
0.1%
0.856 11
 
0.1%
0.766 11
 
0.1%
Other values (561) 1498
 
13.0%
ValueCountFrequency (%)
-1 22626
84.2%
0 107
 
0.4%
0.004 1
 
< 0.1%
0.006 2
 
< 0.1%
0.007 3
 
< 0.1%
0.008 2
 
< 0.1%
0.01 3
 
< 0.1%
0.012 1
 
< 0.1%
0.026 2
 
< 0.1%
0.028 1
 
< 0.1%
ValueCountFrequency (%)
-1 9740
84.5%
0 46
 
0.4%
0.004 1
 
< 0.1%
0.007 1
 
< 0.1%
0.013 1
 
< 0.1%
0.028 1
 
< 0.1%
0.032 1
 
< 0.1%
0.04 1
 
< 0.1%
0.043 2
 
< 0.1%
0.044 2
 
< 0.1%
ValueCountFrequency (%)
-1 9740
36.2%
0 46
 
0.2%
0.004 1
 
< 0.1%
0.007 1
 
< 0.1%
0.013 1
 
< 0.1%
0.028 1
 
< 0.1%
0.032 1
 
< 0.1%
0.04 1
 
< 0.1%
0.043 2
 
< 0.1%
0.044 2
 
< 0.1%
ValueCountFrequency (%)
-1 22626
196.4%
0 107
 
0.9%
0.004 1
 
< 0.1%
0.006 2
 
< 0.1%
0.007 3
 
< 0.1%
0.008 2
 
< 0.1%
0.01 3
 
< 0.1%
0.012 1
 
< 0.1%
0.026 2
 
< 0.1%
0.028 1
 
< 0.1%

PL_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
21662 
1
5219 
0
9343 
1
2178 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row10
2nd row00
3rd row00
4th row00
5th row10

Common Values

ValueCountFrequency (%)
0 21662
80.6%
1 5219
 
19.4%
ValueCountFrequency (%)
0 9343
81.1%
1 2178
 
18.9%

Length

2025-03-10T23:50:26.144005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:26.205470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:26.257813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 21662
80.6%
1 5219
 
19.4%
ValueCountFrequency (%)
0 9343
81.1%
1 2178
 
18.9%

Most occurring characters

ValueCountFrequency (%)
0 21662
80.6%
1 5219
 
19.4%
ValueCountFrequency (%)
0 9343
81.1%
1 2178
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21662
80.6%
1 5219
 
19.4%
ValueCountFrequency (%)
0 9343
81.1%
1 2178
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21662
80.6%
1 5219
 
19.4%
ValueCountFrequency (%)
0 9343
81.1%
1 2178
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21662
80.6%
1 5219
 
19.4%
ValueCountFrequency (%)
0 9343
81.1%
1 2178
 
18.9%

pct_PL_enq_L6m_of_L12m
Real number (ℝ)

 TrainTest
Distinct7355
Distinct (%)0.3%0.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.203087870.20636065
 TrainTest
Minimum00
Maximum11
Zeros205458785
Zeros (%)76.4%76.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:26.349420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.382126870.3856842
Coefficient of variation (CV)1.88158391.8689813
Kurtosis0.272594860.19768033
Mean0.203087870.20636065
Median Absolute Deviation (MAD)00
Skewness1.45856251.4361805
Sum5459.2052377.481
Variance0.146020940.1487523
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:26.467932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20545
76.4%
1 4363
 
16.2%
0.5 655
 
2.4%
0.667 288
 
1.1%
0.333 217
 
0.8%
0.75 132
 
0.5%
0.4 70
 
0.3%
0.8 70
 
0.3%
0.25 61
 
0.2%
0.857 55
 
0.2%
Other values (63) 425
 
1.6%
ValueCountFrequency (%)
0 8785
76.3%
1 1937
 
16.8%
0.5 284
 
2.5%
0.667 135
 
1.2%
0.333 82
 
0.7%
0.75 47
 
0.4%
0.8 24
 
0.2%
0.25 23
 
0.2%
0.833 22
 
0.2%
0.4 21
 
0.2%
Other values (45) 161
 
1.4%
ValueCountFrequency (%)
0 20545
76.4%
0.1 1
 
< 0.1%
0.125 3
 
< 0.1%
0.133 2
 
< 0.1%
0.143 6
 
< 0.1%
0.167 15
 
0.1%
0.2 32
 
0.1%
0.222 1
 
< 0.1%
0.25 61
 
0.2%
0.273 2
 
< 0.1%
ValueCountFrequency (%)
0 8785
76.3%
0.111 1
 
< 0.1%
0.125 1
 
< 0.1%
0.143 7
 
0.1%
0.167 2
 
< 0.1%
0.182 1
 
< 0.1%
0.2 14
 
0.1%
0.222 3
 
< 0.1%
0.25 23
 
0.2%
0.273 1
 
< 0.1%
ValueCountFrequency (%)
0 8785
32.7%
0.111 1
 
< 0.1%
0.125 1
 
< 0.1%
0.143 7
 
< 0.1%
0.167 2
 
< 0.1%
0.182 1
 
< 0.1%
0.2 14
 
0.1%
0.222 3
 
< 0.1%
0.25 23
 
0.1%
0.273 1
 
< 0.1%
ValueCountFrequency (%)
0 20545
178.3%
0.1 1
 
< 0.1%
0.125 3
 
< 0.1%
0.133 2
 
< 0.1%
0.143 6
 
0.1%
0.167 15
 
0.1%
0.2 32
 
0.3%
0.222 1
 
< 0.1%
0.25 61
 
0.5%
0.273 2
 
< 0.1%

pct_CC_enq_L6m_of_L12m
Real number (ℝ)

 TrainTest
Distinct4941
Distinct (%)0.2%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0966273580.097288169
 TrainTest
Minimum00
Maximum11
Zeros2368910141
Zeros (%)88.1%88.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:26.581343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.277801930.2787292
Coefficient of variation (CV)2.87498212.8649856
Kurtosis5.66729615.6115859
Mean0.0966273580.097288169
Median Absolute Deviation (MAD)00
Skewness2.70875312.698812
Sum2597.441120.857
Variance0.077173910.077689968
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:26.693481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 23689
88.1%
1 1962
 
7.3%
0.5 403
 
1.5%
0.667 158
 
0.6%
0.333 157
 
0.6%
0.75 76
 
0.3%
0.25 73
 
0.3%
0.6 45
 
0.2%
0.4 43
 
0.2%
0.8 29
 
0.1%
Other values (39) 246
 
0.9%
ValueCountFrequency (%)
0 10141
88.0%
1 852
 
7.4%
0.5 184
 
1.6%
0.667 74
 
0.6%
0.333 72
 
0.6%
0.75 31
 
0.3%
0.25 26
 
0.2%
0.6 19
 
0.2%
0.2 16
 
0.1%
0.8 15
 
0.1%
Other values (31) 91
 
0.8%
ValueCountFrequency (%)
0 23689
88.1%
0.091 2
 
< 0.1%
0.125 1
 
< 0.1%
0.143 5
 
< 0.1%
0.167 3
 
< 0.1%
0.2 22
 
0.1%
0.222 7
 
< 0.1%
0.25 73
 
0.3%
0.273 3
 
< 0.1%
0.286 11
 
< 0.1%
ValueCountFrequency (%)
0 10141
88.0%
0.1 1
 
< 0.1%
0.167 6
 
0.1%
0.2 16
 
0.1%
0.222 4
 
< 0.1%
0.25 26
 
0.2%
0.273 1
 
< 0.1%
0.286 3
 
< 0.1%
0.3 3
 
< 0.1%
0.333 72
 
0.6%
ValueCountFrequency (%)
0 10141
37.7%
0.1 1
 
< 0.1%
0.167 6
 
< 0.1%
0.2 16
 
0.1%
0.222 4
 
< 0.1%
0.25 26
 
0.1%
0.273 1
 
< 0.1%
0.286 3
 
< 0.1%
0.3 3
 
< 0.1%
0.333 72
 
0.3%
ValueCountFrequency (%)
0 23689
205.6%
0.091 2
 
< 0.1%
0.125 1
 
< 0.1%
0.143 5
 
< 0.1%
0.167 3
 
< 0.1%
0.2 22
 
0.2%
0.222 7
 
0.1%
0.25 73
 
0.6%
0.273 3
 
< 0.1%
0.286 11
 
0.1%

pct_PL_enq_L6m_of_ever
Real number (ℝ)

 TrainTest
Distinct11178
Distinct (%)0.4%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.17596760.17989445
 TrainTest
Minimum00
Maximum11
Zeros205458785
Zeros (%)76.4%76.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:26.808318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.349040740.35330272
Coefficient of variation (CV)1.98355121.9639445
Kurtosis1.15644381.0252407
Mean0.17596760.17989445
Median Absolute Deviation (MAD)00
Skewness1.69582121.6611268
Sum4730.1852072.564
Variance0.121829430.12482281
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:26.928463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20545
76.4%
1 3357
 
12.5%
0.5 826
 
3.1%
0.333 389
 
1.4%
0.667 282
 
1.0%
0.25 218
 
0.8%
0.75 149
 
0.6%
0.4 108
 
0.4%
0.2 101
 
0.4%
0.167 82
 
0.3%
Other values (101) 824
 
3.1%
ValueCountFrequency (%)
0 8785
76.3%
1 1498
 
13.0%
0.5 368
 
3.2%
0.333 164
 
1.4%
0.667 145
 
1.3%
0.25 90
 
0.8%
0.75 53
 
0.5%
0.2 52
 
0.5%
0.4 40
 
0.3%
0.6 28
 
0.2%
Other values (68) 298
 
2.6%
ValueCountFrequency (%)
0 20545
76.4%
0.045 1
 
< 0.1%
0.05 4
 
< 0.1%
0.067 8
 
< 0.1%
0.071 11
 
< 0.1%
0.074 2
 
< 0.1%
0.077 2
 
< 0.1%
0.083 13
 
< 0.1%
0.087 3
 
< 0.1%
0.091 7
 
< 0.1%
ValueCountFrequency (%)
0 8785
76.3%
0.045 2
 
< 0.1%
0.057 1
 
< 0.1%
0.071 1
 
< 0.1%
0.077 1
 
< 0.1%
0.083 4
 
< 0.1%
0.091 2
 
< 0.1%
0.1 8
 
0.1%
0.105 1
 
< 0.1%
0.111 15
 
0.1%
ValueCountFrequency (%)
0 8785
32.7%
0.045 2
 
< 0.1%
0.057 1
 
< 0.1%
0.071 1
 
< 0.1%
0.077 1
 
< 0.1%
0.083 4
 
< 0.1%
0.091 2
 
< 0.1%
0.1 8
 
< 0.1%
0.105 1
 
< 0.1%
0.111 15
 
0.1%
ValueCountFrequency (%)
0 20545
178.3%
0.045 1
 
< 0.1%
0.05 4
 
< 0.1%
0.067 8
 
0.1%
0.071 11
 
0.1%
0.074 2
 
< 0.1%
0.077 2
 
< 0.1%
0.083 13
 
0.1%
0.087 3
 
< 0.1%
0.091 7
 
0.1%

pct_CC_enq_L6m_of_ever
Real number (ℝ)

 TrainTest
Distinct7867
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.07950080.079739606
 TrainTest
Minimum00
Maximum11
Zeros2368910141
Zeros (%)88.1%88.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:27.305539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.243935590.24416181
Coefficient of variation (CV)3.06834143.0619892
Kurtosis8.35824438.333079
Mean0.07950080.079739606
Median Absolute Deviation (MAD)00
Skewness3.11211513.1077971
Sum2137.061918.68
Variance0.0595045740.05961499
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:27.426333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23689
88.1%
1 1401
 
5.2%
0.5 453
 
1.7%
0.333 232
 
0.9%
0.25 154
 
0.6%
0.667 140
 
0.5%
0.2 95
 
0.4%
0.4 77
 
0.3%
0.75 64
 
0.2%
0.167 63
 
0.2%
Other values (68) 513
 
1.9%
ValueCountFrequency (%)
0 10141
88.0%
1 602
 
5.2%
0.5 201
 
1.7%
0.333 96
 
0.8%
0.25 69
 
0.6%
0.667 64
 
0.6%
0.2 43
 
0.4%
0.4 37
 
0.3%
0.167 28
 
0.2%
0.75 23
 
0.2%
Other values (57) 217
 
1.9%
ValueCountFrequency (%)
0 23689
88.1%
0.048 2
 
< 0.1%
0.05 2
 
< 0.1%
0.051 3
 
< 0.1%
0.059 1
 
< 0.1%
0.062 1
 
< 0.1%
0.067 3
 
< 0.1%
0.069 2
 
< 0.1%
0.071 8
 
< 0.1%
0.077 1
 
< 0.1%
ValueCountFrequency (%)
0 10141
88.0%
0.053 1
 
< 0.1%
0.059 3
 
< 0.1%
0.062 4
 
< 0.1%
0.071 2
 
< 0.1%
0.077 3
 
< 0.1%
0.083 4
 
< 0.1%
0.086 2
 
< 0.1%
0.091 12
 
0.1%
0.1 5
 
< 0.1%
ValueCountFrequency (%)
0 10141
37.7%
0.053 1
 
< 0.1%
0.059 3
 
< 0.1%
0.062 4
 
< 0.1%
0.071 2
 
< 0.1%
0.077 3
 
< 0.1%
0.083 4
 
< 0.1%
0.086 2
 
< 0.1%
0.091 12
 
< 0.1%
0.1 5
 
< 0.1%
ValueCountFrequency (%)
0 23689
205.6%
0.048 2
 
< 0.1%
0.05 2
 
< 0.1%
0.051 3
 
< 0.1%
0.059 1
 
< 0.1%
0.062 1
 
< 0.1%
0.067 3
 
< 0.1%
0.069 2
 
< 0.1%
0.071 8
 
0.1%
0.077 1
 
< 0.1%

max_unsec_exposure_inPct
Real number (ℝ)

 TrainTest
Distinct57833712
Distinct (%)21.5%32.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean38.18859410.20411
 TrainTest
Minimum-1-1
Maximum83672.142656.6
Zeros275112
Zeros (%)1.0%1.0%
Negative109294651
Negative (%)40.7%40.4%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:27.542300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile-1-1
Q1-1-1
median0.5670.572
Q32.8572.826
95-th percentile15.93115.217
Maximum83672.142656.6
Range83673.142657.6
Interquartile range (IQR)3.8573.826

Descriptive statistics

 TrainTest
Standard deviation1432.2347425.70768
Coefficient of variation (CV)37.50425441.719235
Kurtosis2766.43198792.4345
Mean38.18859410.20411
Median Absolute Deviation (MAD)1.5671.572
Skewness51.02078289.679411
Sum1026547.6117561.56
Variance2051296.3181227.03
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:27.656615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 10929
40.7%
0 275
 
1.0%
1 75
 
0.3%
5 60
 
0.2%
0.001 56
 
0.2%
2 53
 
0.2%
10 53
 
0.2%
2.5 52
 
0.2%
4 42
 
0.2%
6.667 41
 
0.2%
Other values (5773) 15245
56.7%
ValueCountFrequency (%)
-1 4651
40.4%
0 112
 
1.0%
0.001 40
 
0.3%
10 37
 
0.3%
1 30
 
0.3%
5 26
 
0.2%
3.333 23
 
0.2%
2 22
 
0.2%
2.5 21
 
0.2%
0.6 21
 
0.2%
Other values (3702) 6538
56.7%
ValueCountFrequency (%)
-1 10929
40.7%
0 275
 
1.0%
0.001 56
 
0.2%
0.002 25
 
0.1%
0.003 13
 
< 0.1%
0.004 13
 
< 0.1%
0.005 3
 
< 0.1%
0.006 14
 
0.1%
0.007 6
 
< 0.1%
0.008 10
 
< 0.1%
ValueCountFrequency (%)
-1 4651
40.4%
0 112
 
1.0%
0.001 40
 
0.3%
0.002 14
 
0.1%
0.003 6
 
0.1%
0.004 5
 
< 0.1%
0.005 2
 
< 0.1%
0.006 6
 
0.1%
0.007 4
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
-1 4651
17.3%
0 112
 
0.4%
0.001 40
 
0.1%
0.002 14
 
0.1%
0.003 6
 
< 0.1%
0.004 5
 
< 0.1%
0.005 2
 
< 0.1%
0.006 6
 
< 0.1%
0.007 4
 
< 0.1%
0.008 3
 
< 0.1%
ValueCountFrequency (%)
-1 10929
94.9%
0 275
 
2.4%
0.001 56
 
0.5%
0.002 25
 
0.2%
0.003 13
 
0.1%
0.004 13
 
0.1%
0.005 3
 
< 0.1%
0.006 14
 
0.1%
0.007 6
 
0.1%
0.008 10
 
0.1%

HL_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
19461 
1
7420 
0
8398 
1
3123 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 19461
72.4%
1 7420
 
27.6%
ValueCountFrequency (%)
0 8398
72.9%
1 3123
 
27.1%

Length

2025-03-10T23:50:27.745632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:27.805724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:27.859295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 19461
72.4%
1 7420
 
27.6%
ValueCountFrequency (%)
0 8398
72.9%
1 3123
 
27.1%

Most occurring characters

ValueCountFrequency (%)
0 19461
72.4%
1 7420
 
27.6%
ValueCountFrequency (%)
0 8398
72.9%
1 3123
 
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19461
72.4%
1 7420
 
27.6%
ValueCountFrequency (%)
0 8398
72.9%
1 3123
 
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19461
72.4%
1 7420
 
27.6%
ValueCountFrequency (%)
0 8398
72.9%
1 3123
 
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19461
72.4%
1 7420
 
27.6%
ValueCountFrequency (%)
0 8398
72.9%
1 3123
 
27.1%

GL_Flag
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
0
25377 
1
 
1504
0
10863 
1
 
658

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters2688111521
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st row00
2nd row00
3rd row01
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 25377
94.4%
1 1504
 
5.6%
ValueCountFrequency (%)
0 10863
94.3%
1 658
 
5.7%

Length

2025-03-10T23:50:27.924117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:27.984808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:28.034401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 25377
94.4%
1 1504
 
5.6%
ValueCountFrequency (%)
0 10863
94.3%
1 658
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 25377
94.4%
1 1504
 
5.6%
ValueCountFrequency (%)
0 10863
94.3%
1 658
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25377
94.4%
1 1504
 
5.6%
ValueCountFrequency (%)
0 10863
94.3%
1 658
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25377
94.4%
1 1504
 
5.6%
ValueCountFrequency (%)
0 10863
94.3%
1 658
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26881
100.0%
ValueCountFrequency (%)
(unknown) 11521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25377
94.4%
1 1504
 
5.6%
ValueCountFrequency (%)
0 10863
94.3%
1 658
 
5.7%

last_prod_enq2
Categorical

 TrainTest
Distinct66
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
others
10499 
ConsumerLoan
9059 
PL
4211 
CC
1948 
AL
 
768
others
4369 
ConsumerLoan
3962 
PL
1812 
CC
867 
AL
 
329

Length

 TrainTest
Max length1212
Median length66
Mean length6.93233146.9558198
Min length22

Characters and Unicode

 TrainTest
Total characters18634880138
Distinct characters1515
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowPLothers
2nd rowConsumerLoanConsumerLoan
3rd rowothersConsumerLoan
4th rowConsumerLoanothers
5th rowConsumerLoanothers

Common Values

ValueCountFrequency (%)
others 10499
39.1%
ConsumerLoan 9059
33.7%
PL 4211
15.7%
CC 1948
 
7.2%
AL 768
 
2.9%
HL 396
 
1.5%
ValueCountFrequency (%)
others 4369
37.9%
ConsumerLoan 3962
34.4%
PL 1812
15.7%
CC 867
 
7.5%
AL 329
 
2.9%
HL 182
 
1.6%

Length

2025-03-10T23:50:28.105495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:28.176564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:28.252456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
others 10499
39.1%
consumerloan 9059
33.7%
pl 4211
15.7%
cc 1948
 
7.2%
al 768
 
2.9%
hl 396
 
1.5%
ValueCountFrequency (%)
others 4369
37.9%
consumerloan 3962
34.4%
pl 1812
15.7%
cc 867
 
7.5%
al 329
 
2.9%
hl 182
 
1.6%

Most occurring characters

ValueCountFrequency (%)
o 28617
15.4%
e 19558
10.5%
r 19558
10.5%
s 19558
10.5%
n 18118
9.7%
L 14434
7.7%
C 12955
7.0%
t 10499
 
5.6%
h 10499
 
5.6%
u 9059
 
4.9%
Other values (5) 23493
12.6%
ValueCountFrequency (%)
o 12293
15.3%
e 8331
10.4%
r 8331
10.4%
s 8331
10.4%
n 7924
9.9%
L 6285
7.8%
C 5696
7.1%
t 4369
 
5.5%
h 4369
 
5.5%
u 3962
 
4.9%
Other values (5) 10247
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186348
100.0%
ValueCountFrequency (%)
(unknown) 80138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 28617
15.4%
e 19558
10.5%
r 19558
10.5%
s 19558
10.5%
n 18118
9.7%
L 14434
7.7%
C 12955
7.0%
t 10499
 
5.6%
h 10499
 
5.6%
u 9059
 
4.9%
Other values (5) 23493
12.6%
ValueCountFrequency (%)
o 12293
15.3%
e 8331
10.4%
r 8331
10.4%
s 8331
10.4%
n 7924
9.9%
L 6285
7.8%
C 5696
7.1%
t 4369
 
5.5%
h 4369
 
5.5%
u 3962
 
4.9%
Other values (5) 10247
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186348
100.0%
ValueCountFrequency (%)
(unknown) 80138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 28617
15.4%
e 19558
10.5%
r 19558
10.5%
s 19558
10.5%
n 18118
9.7%
L 14434
7.7%
C 12955
7.0%
t 10499
 
5.6%
h 10499
 
5.6%
u 9059
 
4.9%
Other values (5) 23493
12.6%
ValueCountFrequency (%)
o 12293
15.3%
e 8331
10.4%
r 8331
10.4%
s 8331
10.4%
n 7924
9.9%
L 6285
7.8%
C 5696
7.1%
t 4369
 
5.5%
h 4369
 
5.5%
u 3962
 
4.9%
Other values (5) 10247
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186348
100.0%
ValueCountFrequency (%)
(unknown) 80138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 28617
15.4%
e 19558
10.5%
r 19558
10.5%
s 19558
10.5%
n 18118
9.7%
L 14434
7.7%
C 12955
7.0%
t 10499
 
5.6%
h 10499
 
5.6%
u 9059
 
4.9%
Other values (5) 23493
12.6%
ValueCountFrequency (%)
o 12293
15.3%
e 8331
10.4%
r 8331
10.4%
s 8331
10.4%
n 7924
9.9%
L 6285
7.8%
C 5696
7.1%
t 4369
 
5.5%
h 4369
 
5.5%
u 3962
 
4.9%
Other values (5) 10247
12.8%

first_prod_enq2
Categorical

 TrainTest
Distinct66
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
others
14117 
ConsumerLoan
5994 
PL
2599 
CC
1902 
AL
1577 
others
5988 
ConsumerLoan
2602 
PL
1162 
CC
792 
AL
645 

Length

 TrainTest
Max length1212
Median length66
Mean length6.33049376.3374707
Min length22

Characters and Unicode

 TrainTest
Total characters17017073014
Distinct characters1515
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowothersothers
2nd rowothersConsumerLoan
3rd rowothersCC
4th rowothersothers
5th rowConsumerLoanothers

Common Values

ValueCountFrequency (%)
others 14117
52.5%
ConsumerLoan 5994
22.3%
PL 2599
 
9.7%
CC 1902
 
7.1%
AL 1577
 
5.9%
HL 692
 
2.6%
ValueCountFrequency (%)
others 5988
52.0%
ConsumerLoan 2602
22.6%
PL 1162
 
10.1%
CC 792
 
6.9%
AL 645
 
5.6%
HL 332
 
2.9%

Length

2025-03-10T23:50:28.335540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2025-03-10T23:50:28.407686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:28.481880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
others 14117
52.5%
consumerloan 5994
22.3%
pl 2599
 
9.7%
cc 1902
 
7.1%
al 1577
 
5.9%
hl 692
 
2.6%
ValueCountFrequency (%)
others 5988
52.0%
consumerloan 2602
22.6%
pl 1162
 
10.1%
cc 792
 
6.9%
al 645
 
5.6%
hl 332
 
2.9%

Most occurring characters

ValueCountFrequency (%)
o 26105
15.3%
e 20111
11.8%
r 20111
11.8%
s 20111
11.8%
t 14117
8.3%
h 14117
8.3%
n 11988
7.0%
L 10862
6.4%
C 9798
 
5.8%
u 5994
 
3.5%
Other values (5) 16856
9.9%
ValueCountFrequency (%)
o 11192
15.3%
e 8590
11.8%
r 8590
11.8%
s 8590
11.8%
t 5988
8.2%
h 5988
8.2%
n 5204
7.1%
L 4741
6.5%
C 4186
 
5.7%
u 2602
 
3.6%
Other values (5) 7343
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 170170
100.0%
ValueCountFrequency (%)
(unknown) 73014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26105
15.3%
e 20111
11.8%
r 20111
11.8%
s 20111
11.8%
t 14117
8.3%
h 14117
8.3%
n 11988
7.0%
L 10862
6.4%
C 9798
 
5.8%
u 5994
 
3.5%
Other values (5) 16856
9.9%
ValueCountFrequency (%)
o 11192
15.3%
e 8590
11.8%
r 8590
11.8%
s 8590
11.8%
t 5988
8.2%
h 5988
8.2%
n 5204
7.1%
L 4741
6.5%
C 4186
 
5.7%
u 2602
 
3.6%
Other values (5) 7343
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 170170
100.0%
ValueCountFrequency (%)
(unknown) 73014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26105
15.3%
e 20111
11.8%
r 20111
11.8%
s 20111
11.8%
t 14117
8.3%
h 14117
8.3%
n 11988
7.0%
L 10862
6.4%
C 9798
 
5.8%
u 5994
 
3.5%
Other values (5) 16856
9.9%
ValueCountFrequency (%)
o 11192
15.3%
e 8590
11.8%
r 8590
11.8%
s 8590
11.8%
t 5988
8.2%
h 5988
8.2%
n 5204
7.1%
L 4741
6.5%
C 4186
 
5.7%
u 2602
 
3.6%
Other values (5) 7343
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 170170
100.0%
ValueCountFrequency (%)
(unknown) 73014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26105
15.3%
e 20111
11.8%
r 20111
11.8%
s 20111
11.8%
t 14117
8.3%
h 14117
8.3%
n 11988
7.0%
L 10862
6.4%
C 9798
 
5.8%
u 5994
 
3.5%
Other values (5) 16856
9.9%
ValueCountFrequency (%)
o 11192
15.3%
e 8590
11.8%
r 8590
11.8%
s 8590
11.8%
t 5988
8.2%
h 5988
8.2%
n 5204
7.1%
L 4741
6.5%
C 4186
 
5.7%
u 2602
 
3.6%
Other values (5) 7343
10.1%

Credit_Score
Real number (ℝ)

 TrainTest
Distinct217191
Distinct (%)0.8%1.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean679.5146679.74837
 TrainTest
Minimum469509
Maximum811811
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:28.582184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum469509
5-th percentile648648
Q1668668
median679679
Q3690691
95-th percentile714714
Maximum811811
Range342302
Interquartile range (IQR)2223

Descriptive statistics

 TrainTest
Standard deviation21.21264420.815079
Coefficient of variation (CV)0.0312173490.030621741
Kurtosis4.92421543.7683018
Mean679.5146679.74837
Median Absolute Deviation (MAD)1111
Skewness-0.35973311-0.12074858
Sum182660327831381
Variance449.97628433.26749
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:28.696368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
679 734
 
2.7%
682 727
 
2.7%
678 700
 
2.6%
672 660
 
2.5%
680 653
 
2.4%
681 637
 
2.4%
684 636
 
2.4%
673 624
 
2.3%
683 618
 
2.3%
686 612
 
2.3%
Other values (207) 20280
75.4%
ValueCountFrequency (%)
679 319
 
2.8%
682 311
 
2.7%
680 300
 
2.6%
678 296
 
2.6%
681 284
 
2.5%
674 282
 
2.4%
676 278
 
2.4%
672 273
 
2.4%
683 267
 
2.3%
687 258
 
2.2%
Other values (181) 8653
75.1%
ValueCountFrequency (%)
469 2
< 0.1%
499 1
 
< 0.1%
509 2
< 0.1%
511 1
 
< 0.1%
513 1
 
< 0.1%
520 1
 
< 0.1%
529 1
 
< 0.1%
531 2
< 0.1%
538 3
< 0.1%
539 1
 
< 0.1%
ValueCountFrequency (%)
509 1
< 0.1%
513 1
< 0.1%
520 1
< 0.1%
536 1
< 0.1%
538 1
< 0.1%
546 2
< 0.1%
548 1
< 0.1%
553 1
< 0.1%
568 1
< 0.1%
581 1
< 0.1%
ValueCountFrequency (%)
509 1
< 0.1%
513 1
< 0.1%
520 1
< 0.1%
536 1
< 0.1%
538 1
< 0.1%
546 2
< 0.1%
548 1
< 0.1%
553 1
< 0.1%
568 1
< 0.1%
581 1
< 0.1%
ValueCountFrequency (%)
469 2
< 0.1%
499 1
 
< 0.1%
509 2
< 0.1%
511 1
 
< 0.1%
513 1
 
< 0.1%
520 1
 
< 0.1%
529 1
 
< 0.1%
531 2
< 0.1%
538 3
< 0.1%
539 1
 
< 0.1%

Total_TL
Real number (ℝ)

 TrainTest
Distinct8475
Distinct (%)0.3%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5.32405795.2435552
 TrainTest
Minimum11
Maximum235235
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:28.813344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum11
5-th percentile11
Q111
median33
Q366
95-th percentile1818
Maximum235235
Range234234
Interquartile range (IQR)55

Descriptive statistics

 TrainTest
Standard deviation7.62006127.652674
Coefficient of variation (CV)1.43125061.4594438
Kurtosis67.935641104.97264
Mean5.32405795.2435552
Median Absolute Deviation (MAD)22
Skewness5.51861476.6207785
Sum14311660411
Variance58.06533258.563419
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:28.927501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7984
29.7%
2 4647
17.3%
3 3156
 
11.7%
4 2072
 
7.7%
5 1500
 
5.6%
6 1217
 
4.5%
7 921
 
3.4%
8 753
 
2.8%
9 637
 
2.4%
10 475
 
1.8%
Other values (74) 3519
13.1%
ValueCountFrequency (%)
1 3489
30.3%
2 1974
17.1%
3 1286
 
11.2%
4 893
 
7.8%
5 697
 
6.0%
6 545
 
4.7%
7 391
 
3.4%
8 309
 
2.7%
9 291
 
2.5%
10 205
 
1.8%
Other values (65) 1441
12.5%
ValueCountFrequency (%)
1 7984
29.7%
2 4647
17.3%
3 3156
 
11.7%
4 2072
 
7.7%
5 1500
 
5.6%
6 1217
 
4.5%
7 921
 
3.4%
8 753
 
2.8%
9 637
 
2.4%
10 475
 
1.8%
ValueCountFrequency (%)
1 3489
30.3%
2 1974
17.1%
3 1286
 
11.2%
4 893
 
7.8%
5 697
 
6.0%
6 545
 
4.7%
7 391
 
3.4%
8 309
 
2.7%
9 291
 
2.5%
10 205
 
1.8%
ValueCountFrequency (%)
1 3489
13.0%
2 1974
7.3%
3 1286
 
4.8%
4 893
 
3.3%
5 697
 
2.6%
6 545
 
2.0%
7 391
 
1.5%
8 309
 
1.1%
9 291
 
1.1%
10 205
 
0.8%
ValueCountFrequency (%)
1 7984
69.3%
2 4647
40.3%
3 3156
 
27.4%
4 2072
 
18.0%
5 1500
 
13.0%
6 1217
 
10.6%
7 921
 
8.0%
8 753
 
6.5%
9 637
 
5.5%
10 475
 
4.1%

Tot_Closed_TL
Real number (ℝ)

 TrainTest
Distinct7364
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.96320822.923097
 TrainTest
Minimum00
Maximum216216
Zeros94404047
Zeros (%)35.1%35.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:29.040730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q333
95-th percentile1312
Maximum216216
Range216216
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation6.10010316.2197732
Coefficient of variation (CV)2.05861442.1278026
Kurtosis115.58486169.1179
Mean2.96320822.923097
Median Absolute Deviation (MAD)11
Skewness7.24748888.6210426
Sum7965433677
Variance37.21125838.685578
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:29.155145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9440
35.1%
1 6536
24.3%
2 3035
 
11.3%
3 1785
 
6.6%
4 1186
 
4.4%
5 937
 
3.5%
6 669
 
2.5%
7 532
 
2.0%
8 364
 
1.4%
9 335
 
1.2%
Other values (63) 2062
 
7.7%
ValueCountFrequency (%)
0 4047
35.1%
1 2811
24.4%
2 1342
 
11.6%
3 780
 
6.8%
4 489
 
4.2%
5 410
 
3.6%
6 281
 
2.4%
7 222
 
1.9%
8 161
 
1.4%
9 121
 
1.1%
Other values (54) 857
 
7.4%
ValueCountFrequency (%)
0 9440
35.1%
1 6536
24.3%
2 3035
 
11.3%
3 1785
 
6.6%
4 1186
 
4.4%
5 937
 
3.5%
6 669
 
2.5%
7 532
 
2.0%
8 364
 
1.4%
9 335
 
1.2%
ValueCountFrequency (%)
0 4047
35.1%
1 2811
24.4%
2 1342
 
11.6%
3 780
 
6.8%
4 489
 
4.2%
5 410
 
3.6%
6 281
 
2.4%
7 222
 
1.9%
8 161
 
1.4%
9 121
 
1.1%
ValueCountFrequency (%)
0 4047
15.1%
1 2811
10.5%
2 1342
 
5.0%
3 780
 
2.9%
4 489
 
1.8%
5 410
 
1.5%
6 281
 
1.0%
7 222
 
0.8%
8 161
 
0.6%
9 121
 
0.5%
ValueCountFrequency (%)
0 9440
81.9%
1 6536
56.7%
2 3035
 
26.3%
3 1785
 
15.5%
4 1186
 
10.3%
5 937
 
8.1%
6 669
 
5.8%
7 532
 
4.6%
8 364
 
3.2%
9 335
 
2.9%

Tot_Active_TL
Real number (ℝ)

 TrainTest
Distinct2927
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.36084972.3204583
 TrainTest
Minimum00
Maximum4737
Zeros40771682
Zeros (%)15.2%14.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:29.254385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q111
median21
Q333
95-th percentile77
Maximum4737
Range4737
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation2.60298132.5100643
Coefficient of variation (CV)1.10256121.0817106
Kurtosis15.72764312.786011
Mean2.36084972.3204583
Median Absolute Deviation (MAD)11
Skewness2.85918512.6975498
Sum6346226734
Variance6.77551186.3004226
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:29.349073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 9261
34.5%
2 5051
18.8%
0 4077
15.2%
3 2934
 
10.9%
4 1718
 
6.4%
5 1167
 
4.3%
6 828
 
3.1%
7 555
 
2.1%
8 409
 
1.5%
9 256
 
1.0%
Other values (19) 625
 
2.3%
ValueCountFrequency (%)
1 4119
35.8%
2 2105
18.3%
0 1682
14.6%
3 1283
 
11.1%
4 752
 
6.5%
5 492
 
4.3%
6 339
 
2.9%
7 227
 
2.0%
8 173
 
1.5%
9 98
 
0.9%
Other values (17) 251
 
2.2%
ValueCountFrequency (%)
0 4077
15.2%
1 9261
34.5%
2 5051
18.8%
3 2934
 
10.9%
4 1718
 
6.4%
5 1167
 
4.3%
6 828
 
3.1%
7 555
 
2.1%
8 409
 
1.5%
9 256
 
1.0%
ValueCountFrequency (%)
0 1682
14.6%
1 4119
35.8%
2 2105
18.3%
3 1283
 
11.1%
4 752
 
6.5%
5 492
 
4.3%
6 339
 
2.9%
7 227
 
2.0%
8 173
 
1.5%
9 98
 
0.9%
ValueCountFrequency (%)
0 1682
6.3%
1 4119
15.3%
2 2105
7.8%
3 1283
 
4.8%
4 752
 
2.8%
5 492
 
1.8%
6 339
 
1.3%
7 227
 
0.8%
8 173
 
0.6%
9 98
 
0.4%
ValueCountFrequency (%)
0 4077
35.4%
1 9261
80.4%
2 5051
43.8%
3 2934
 
25.5%
4 1718
 
14.9%
5 1167
 
10.1%
6 828
 
7.2%
7 555
 
4.8%
8 409
 
3.6%
9 256
 
2.2%

Total_TL_opened_L6M
Real number (ℝ)

 TrainTest
Distinct1817
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.812730180.80331568
 TrainTest
Minimum00
Maximum1818
Zeros154216654
Zeros (%)57.4%57.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:29.431633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile33
Maximum1818
Range1818
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.39601481.3767134
Coefficient of variation (CV)1.71768541.7137887
Kurtosis16.94342316.052434
Mean0.812730180.80331568
Median Absolute Deviation (MAD)00
Skewness3.24212623.1816688
Sum218479255
Variance1.94885731.8953397
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:29.518885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 15421
57.4%
1 6666
24.8%
2 2368
 
8.8%
3 1140
 
4.2%
4 556
 
2.1%
5 306
 
1.1%
6 138
 
0.5%
7 128
 
0.5%
8 64
 
0.2%
9 26
 
0.1%
Other values (8) 68
 
0.3%
ValueCountFrequency (%)
0 6654
57.8%
1 2786
24.2%
2 1084
 
9.4%
3 460
 
4.0%
4 228
 
2.0%
5 127
 
1.1%
6 76
 
0.7%
7 48
 
0.4%
8 19
 
0.2%
11 12
 
0.1%
Other values (7) 27
 
0.2%
ValueCountFrequency (%)
0 15421
57.4%
1 6666
24.8%
2 2368
 
8.8%
3 1140
 
4.2%
4 556
 
2.1%
5 306
 
1.1%
6 138
 
0.5%
7 128
 
0.5%
8 64
 
0.2%
9 26
 
0.1%
ValueCountFrequency (%)
0 6654
57.8%
1 2786
24.2%
2 1084
 
9.4%
3 460
 
4.0%
4 228
 
2.0%
5 127
 
1.1%
6 76
 
0.7%
7 48
 
0.4%
8 19
 
0.2%
9 9
 
0.1%
ValueCountFrequency (%)
0 6654
24.8%
1 2786
10.4%
2 1084
 
4.0%
3 460
 
1.7%
4 228
 
0.8%
5 127
 
0.5%
6 76
 
0.3%
7 48
 
0.2%
8 19
 
0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
0 15421
133.9%
1 6666
57.9%
2 2368
 
20.6%
3 1140
 
9.9%
4 556
 
4.8%
5 306
 
2.7%
6 138
 
1.2%
7 128
 
1.1%
8 64
 
0.6%
9 26
 
0.2%

Tot_TL_closed_L6M
Real number (ℝ)

 TrainTest
Distinct1616
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.471745840.47200764
 TrainTest
Minimum00
Maximum1918
Zeros195938371
Zeros (%)72.9%72.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:29.605522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile22
Maximum1918
Range1918
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.07682551.0845835
Coefficient of variation (CV)2.28263922.2978092
Kurtosis29.63564732.578272
Mean0.471745840.47200764
Median Absolute Deviation (MAD)00
Skewness4.27860634.4947366
Sum126815438
Variance1.15955321.1763214
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:29.694798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 19593
72.9%
1 4715
 
17.5%
2 1352
 
5.0%
3 542
 
2.0%
4 311
 
1.2%
5 165
 
0.6%
6 78
 
0.3%
7 41
 
0.2%
8 27
 
0.1%
9 26
 
0.1%
Other values (6) 31
 
0.1%
ValueCountFrequency (%)
0 8371
72.7%
1 2048
 
17.8%
2 617
 
5.4%
3 211
 
1.8%
4 109
 
0.9%
5 65
 
0.6%
6 45
 
0.4%
7 20
 
0.2%
8 12
 
0.1%
9 7
 
0.1%
Other values (6) 16
 
0.1%
ValueCountFrequency (%)
0 19593
72.9%
1 4715
 
17.5%
2 1352
 
5.0%
3 542
 
2.0%
4 311
 
1.2%
5 165
 
0.6%
6 78
 
0.3%
7 41
 
0.2%
8 27
 
0.1%
9 26
 
0.1%
ValueCountFrequency (%)
0 8371
72.7%
1 2048
 
17.8%
2 617
 
5.4%
3 211
 
1.8%
4 109
 
0.9%
5 65
 
0.6%
6 45
 
0.4%
7 20
 
0.2%
8 12
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
0 8371
31.1%
1 2048
 
7.6%
2 617
 
2.3%
3 211
 
0.8%
4 109
 
0.4%
5 65
 
0.2%
6 45
 
0.2%
7 20
 
0.1%
8 12
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
0 19593
170.1%
1 4715
 
40.9%
2 1352
 
11.7%
3 542
 
4.7%
4 311
 
2.7%
5 165
 
1.4%
6 78
 
0.7%
7 41
 
0.4%
8 27
 
0.2%
9 26
 
0.2%

pct_tl_open_L6M
Real number (ℝ)

 TrainTest
Distinct227188
Distinct (%)0.8%1.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.185712180.18446376
 TrainTest
Minimum00
Maximum11
Zeros154216654
Zeros (%)57.4%57.8%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:29.808928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.3120.324
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.3120.324

Descriptive statistics

 TrainTest
Standard deviation0.291792380.29184159
Coefficient of variation (CV)1.57120761.582108
Kurtosis1.66661261.7162148
Mean0.185712180.18446376
Median Absolute Deviation (MAD)00
Skewness1.63342981.6486066
Sum4992.1292125.207
Variance0.0851427950.085171512
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:29.934089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15421
57.4%
0.5 1908
 
7.1%
1 1795
 
6.7%
0.333 1222
 
4.5%
0.25 807
 
3.0%
0.2 546
 
2.0%
0.667 521
 
1.9%
0.167 401
 
1.5%
0.143 294
 
1.1%
0.4 268
 
1.0%
Other values (217) 3698
 
13.8%
ValueCountFrequency (%)
0 6654
57.8%
0.5 828
 
7.2%
1 777
 
6.7%
0.333 520
 
4.5%
0.25 297
 
2.6%
0.2 242
 
2.1%
0.667 198
 
1.7%
0.167 176
 
1.5%
0.4 142
 
1.2%
0.143 136
 
1.2%
Other values (178) 1551
 
13.5%
ValueCountFrequency (%)
0 15421
57.4%
0.01 4
 
< 0.1%
0.013 1
 
< 0.1%
0.015 1
 
< 0.1%
0.016 2
 
< 0.1%
0.017 3
 
< 0.1%
0.019 3
 
< 0.1%
0.02 5
 
< 0.1%
0.021 1
 
< 0.1%
0.022 4
 
< 0.1%
ValueCountFrequency (%)
0 6654
57.8%
0.01 1
 
< 0.1%
0.013 2
 
< 0.1%
0.014 2
 
< 0.1%
0.016 3
 
< 0.1%
0.02 1
 
< 0.1%
0.022 1
 
< 0.1%
0.023 2
 
< 0.1%
0.024 3
 
< 0.1%
0.025 2
 
< 0.1%
ValueCountFrequency (%)
0 6654
24.8%
0.01 1
 
< 0.1%
0.013 2
 
< 0.1%
0.014 2
 
< 0.1%
0.016 3
 
< 0.1%
0.02 1
 
< 0.1%
0.022 1
 
< 0.1%
0.023 2
 
< 0.1%
0.024 3
 
< 0.1%
0.025 2
 
< 0.1%
ValueCountFrequency (%)
0 15421
133.9%
0.01 4
 
< 0.1%
0.013 1
 
< 0.1%
0.015 1
 
< 0.1%
0.016 2
 
< 0.1%
0.017 3
 
< 0.1%
0.019 3
 
< 0.1%
0.02 5
 
< 0.1%
0.021 1
 
< 0.1%
0.022 4
 
< 0.1%

pct_tl_closed_L6M
Real number (ℝ)

 TrainTest
Distinct204159
Distinct (%)0.8%1.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0873529260.087087058
 TrainTest
Minimum00
Maximum11
Zeros195938371
Zeros (%)72.9%72.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:30.057663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.0710.077
95-th percentile0.50.5
Maximum11
Range11
Interquartile range (IQR)0.0710.077

Descriptive statistics

 TrainTest
Standard deviation0.199610840.19752418
Coefficient of variation (CV)2.28510772.2681232
Kurtosis9.76287279.8052527
Mean0.0873529260.087087058
Median Absolute Deviation (MAD)00
Skewness3.03819053.0319629
Sum2348.1341003.33
Variance0.0398444890.0390158
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:30.184890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19593
72.9%
0.5 837
 
3.1%
0.333 752
 
2.8%
1 701
 
2.6%
0.25 641
 
2.4%
0.2 492
 
1.8%
0.167 418
 
1.6%
0.143 322
 
1.2%
0.125 247
 
0.9%
0.111 237
 
0.9%
Other values (194) 2641
 
9.8%
ValueCountFrequency (%)
0 8371
72.7%
0.5 367
 
3.2%
0.333 306
 
2.7%
1 287
 
2.5%
0.25 270
 
2.3%
0.2 261
 
2.3%
0.167 190
 
1.6%
0.143 151
 
1.3%
0.111 115
 
1.0%
0.125 114
 
1.0%
Other values (149) 1089
 
9.5%
ValueCountFrequency (%)
0 19593
72.9%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.016 2
 
< 0.1%
0.017 2
 
< 0.1%
0.018 1
 
< 0.1%
0.019 2
 
< 0.1%
0.02 5
 
< 0.1%
0.021 8
 
< 0.1%
0.022 2
 
< 0.1%
ValueCountFrequency (%)
0 8371
72.7%
0.008 1
 
< 0.1%
0.01 1
 
< 0.1%
0.013 2
 
< 0.1%
0.016 2
 
< 0.1%
0.017 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 1
 
< 0.1%
0.023 4
 
< 0.1%
0.024 3
 
< 0.1%
ValueCountFrequency (%)
0 8371
31.1%
0.008 1
 
< 0.1%
0.01 1
 
< 0.1%
0.013 2
 
< 0.1%
0.016 2
 
< 0.1%
0.017 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 1
 
< 0.1%
0.023 4
 
< 0.1%
0.024 3
 
< 0.1%
ValueCountFrequency (%)
0 19593
170.1%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.016 2
 
< 0.1%
0.017 2
 
< 0.1%
0.018 1
 
< 0.1%
0.019 2
 
< 0.1%
0.02 5
 
< 0.1%
0.021 8
 
0.1%
0.022 2
 
< 0.1%

pct_active_tl
Real number (ℝ)

 TrainTest
Distinct332265
Distinct (%)1.2%2.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.58464190.58885383
 TrainTest
Minimum00
Maximum11
Zeros40771682
Zeros (%)15.2%14.6%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:30.305815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q10.2860.286
median0.5950.6
Q311
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.7140.714

Descriptive statistics

 TrainTest
Standard deviation0.372464410.369496
Coefficient of variation (CV)0.637081270.62748339
Kurtosis-1.3575591-1.3313392
Mean0.58464190.58885383
Median Absolute Deviation (MAD)0.4050.4
Skewness-0.24785968-0.26136069
Sum15715.7596784.185
Variance0.138729730.13652729
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:30.422812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9440
35.1%
0 4077
15.2%
0.5 2915
 
10.8%
0.667 1440
 
5.4%
0.333 1229
 
4.6%
0.75 676
 
2.5%
0.25 602
 
2.2%
0.6 430
 
1.6%
0.4 430
 
1.6%
0.2 348
 
1.3%
Other values (322) 5294
19.7%
ValueCountFrequency (%)
1 4047
35.1%
0 1682
14.6%
0.5 1318
 
11.4%
0.667 601
 
5.2%
0.333 503
 
4.4%
0.25 279
 
2.4%
0.75 278
 
2.4%
0.6 223
 
1.9%
0.4 198
 
1.7%
0.8 151
 
1.3%
Other values (255) 2241
19.5%
ValueCountFrequency (%)
0 4077
15.2%
0.016 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 2
 
< 0.1%
0.022 1
 
< 0.1%
0.025 2
 
< 0.1%
0.026 1
 
< 0.1%
0.027 1
 
< 0.1%
0.029 3
 
< 0.1%
0.031 4
 
< 0.1%
ValueCountFrequency (%)
0 1682
14.6%
0.016 1
 
< 0.1%
0.017 1
 
< 0.1%
0.019 1
 
< 0.1%
0.02 1
 
< 0.1%
0.021 1
 
< 0.1%
0.024 1
 
< 0.1%
0.025 1
 
< 0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
ValueCountFrequency (%)
0 1682
6.3%
0.016 1
 
< 0.1%
0.017 1
 
< 0.1%
0.019 1
 
< 0.1%
0.02 1
 
< 0.1%
0.021 1
 
< 0.1%
0.024 1
 
< 0.1%
0.025 1
 
< 0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
ValueCountFrequency (%)
0 4077
35.4%
0.016 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 2
 
< 0.1%
0.022 1
 
< 0.1%
0.025 2
 
< 0.1%
0.026 1
 
< 0.1%
0.027 1
 
< 0.1%
0.029 3
 
< 0.1%
0.031 4
 
< 0.1%

pct_closed_tl
Real number (ℝ)

 TrainTest
Distinct332265
Distinct (%)1.2%2.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.41535810.41114617
 TrainTest
Minimum00
Maximum11
Zeros94404047
Zeros (%)35.1%35.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:30.539724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median0.4050.4
Q30.7140.714
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.7140.714

Descriptive statistics

 TrainTest
Standard deviation0.372464410.369496
Coefficient of variation (CV)0.896730820.89869741
Kurtosis-1.3575591-1.3313392
Mean0.41535810.41114617
Median Absolute Deviation (MAD)0.4050.4
Skewness0.247859680.26136069
Sum11165.2414736.815
Variance0.138729730.13652729
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:30.663621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9440
35.1%
1 4077
15.2%
0.5 2915
 
10.8%
0.333 1440
 
5.4%
0.667 1229
 
4.6%
0.25 676
 
2.5%
0.75 602
 
2.2%
0.4 430
 
1.6%
0.6 430
 
1.6%
0.8 348
 
1.3%
Other values (322) 5294
19.7%
ValueCountFrequency (%)
0 4047
35.1%
1 1682
14.6%
0.5 1318
 
11.4%
0.333 601
 
5.2%
0.667 503
 
4.4%
0.75 279
 
2.4%
0.25 278
 
2.4%
0.4 223
 
1.9%
0.6 198
 
1.7%
0.2 151
 
1.3%
Other values (255) 2241
19.5%
ValueCountFrequency (%)
0 9440
35.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.067 1
 
< 0.1%
0.071 3
 
< 0.1%
0.077 3
 
< 0.1%
0.083 5
 
< 0.1%
0.091 10
 
< 0.1%
0.1 19
 
0.1%
0.111 41
 
0.2%
ValueCountFrequency (%)
0 4047
35.1%
0.062 1
 
< 0.1%
0.067 1
 
< 0.1%
0.077 2
 
< 0.1%
0.083 4
 
< 0.1%
0.091 4
 
< 0.1%
0.1 6
 
0.1%
0.111 26
 
0.2%
0.118 1
 
< 0.1%
0.125 19
 
0.2%
ValueCountFrequency (%)
0 4047
15.1%
0.062 1
 
< 0.1%
0.067 1
 
< 0.1%
0.077 2
 
< 0.1%
0.083 4
 
< 0.1%
0.091 4
 
< 0.1%
0.1 6
 
< 0.1%
0.111 26
 
0.1%
0.118 1
 
< 0.1%
0.125 19
 
0.1%
ValueCountFrequency (%)
0 9440
81.9%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.067 1
 
< 0.1%
0.071 3
 
< 0.1%
0.077 3
 
< 0.1%
0.083 5
 
< 0.1%
0.091 10
 
0.1%
0.1 19
 
0.2%
0.111 41
 
0.4%

Total_TL_opened_L12M
Real number (ℝ)

 TrainTest
Distinct3227
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.66225211.6467321
 TrainTest
Minimum00
Maximum3433
Zeros89563852
Zeros (%)33.3%33.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:30.770435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q322
95-th percentile66
Maximum3433
Range3433
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation2.33919272.3150736
Coefficient of variation (CV)1.4072431.4058593
Kurtosis20.51170620.579484
Mean1.66225211.6467321
Median Absolute Deviation (MAD)11
Skewness3.44388253.4460603
Sum4468318972
Variance5.47182245.3595659
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:30.877375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 8956
33.3%
1 8459
31.5%
2 3964
14.7%
3 2003
 
7.5%
4 1166
 
4.3%
5 711
 
2.6%
6 486
 
1.8%
7 365
 
1.4%
8 189
 
0.7%
9 142
 
0.5%
Other values (22) 440
 
1.6%
ValueCountFrequency (%)
0 3852
33.4%
1 3638
31.6%
2 1703
14.8%
3 835
 
7.2%
4 513
 
4.5%
5 316
 
2.7%
6 191
 
1.7%
7 149
 
1.3%
8 78
 
0.7%
9 71
 
0.6%
Other values (17) 175
 
1.5%
ValueCountFrequency (%)
0 8956
33.3%
1 8459
31.5%
2 3964
14.7%
3 2003
 
7.5%
4 1166
 
4.3%
5 711
 
2.6%
6 486
 
1.8%
7 365
 
1.4%
8 189
 
0.7%
9 142
 
0.5%
ValueCountFrequency (%)
0 3852
33.4%
1 3638
31.6%
2 1703
14.8%
3 835
 
7.2%
4 513
 
4.5%
5 316
 
2.7%
6 191
 
1.7%
7 149
 
1.3%
8 78
 
0.7%
9 71
 
0.6%
ValueCountFrequency (%)
0 3852
14.3%
1 3638
13.5%
2 1703
6.3%
3 835
 
3.1%
4 513
 
1.9%
5 316
 
1.2%
6 191
 
0.7%
7 149
 
0.6%
8 78
 
0.3%
9 71
 
0.3%
ValueCountFrequency (%)
0 8956
77.7%
1 8459
73.4%
2 3964
34.4%
3 2003
 
17.4%
4 1166
 
10.1%
5 711
 
6.2%
6 486
 
4.2%
7 365
 
3.2%
8 189
 
1.6%
9 142
 
1.2%

Tot_TL_closed_L12M
Real number (ℝ)

 TrainTest
Distinct2321
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.800007440.79871539
 TrainTest
Minimum00
Maximum3327
Zeros163106996
Zeros (%)60.7%60.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:30.978554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile33
Maximum3327
Range3327
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.57000391.5378958
Coefficient of variation (CV)1.96248661.9254615
Kurtosis37.91861128.467992
Mean0.800007440.79871539
Median Absolute Deviation (MAD)00
Skewness4.4805874.0738376
Sum215059202
Variance2.46491222.3651233
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:31.074758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 16310
60.7%
1 6035
 
22.5%
2 2215
 
8.2%
3 985
 
3.7%
4 473
 
1.8%
5 295
 
1.1%
6 177
 
0.7%
7 122
 
0.5%
8 82
 
0.3%
9 64
 
0.2%
Other values (13) 123
 
0.5%
ValueCountFrequency (%)
0 6996
60.7%
1 2541
 
22.1%
2 969
 
8.4%
3 447
 
3.9%
4 228
 
2.0%
5 90
 
0.8%
6 80
 
0.7%
7 58
 
0.5%
8 43
 
0.4%
9 24
 
0.2%
Other values (11) 45
 
0.4%
ValueCountFrequency (%)
0 16310
60.7%
1 6035
 
22.5%
2 2215
 
8.2%
3 985
 
3.7%
4 473
 
1.8%
5 295
 
1.1%
6 177
 
0.7%
7 122
 
0.5%
8 82
 
0.3%
9 64
 
0.2%
ValueCountFrequency (%)
0 6996
60.7%
1 2541
 
22.1%
2 969
 
8.4%
3 447
 
3.9%
4 228
 
2.0%
5 90
 
0.8%
6 80
 
0.7%
7 58
 
0.5%
8 43
 
0.4%
9 24
 
0.2%
ValueCountFrequency (%)
0 6996
26.0%
1 2541
 
9.5%
2 969
 
3.6%
3 447
 
1.7%
4 228
 
0.8%
5 90
 
0.3%
6 80
 
0.3%
7 58
 
0.2%
8 43
 
0.2%
9 24
 
0.1%
ValueCountFrequency (%)
0 16310
141.6%
1 6035
 
52.4%
2 2215
 
19.2%
3 985
 
8.5%
4 473
 
4.1%
5 295
 
2.6%
6 177
 
1.5%
7 122
 
1.1%
8 82
 
0.7%
9 64
 
0.6%

pct_tl_open_L12M
Real number (ℝ)

 TrainTest
Distinct315241
Distinct (%)1.2%2.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.394914180.39510598
 TrainTest
Minimum00
Maximum11
Zeros89563852
Zeros (%)33.3%33.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:31.201219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median0.3330.333
Q30.70.714
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0.70.714

Descriptive statistics

 TrainTest
Standard deviation0.385025680.38502784
Coefficient of variation (CV)0.97496040.97449257
Kurtosis-1.2701053-1.265714
Mean0.394914180.39510598
Median Absolute Deviation (MAD)0.3330.333
Skewness0.478682060.48136926
Sum10615.6884552.016
Variance0.148244780.14824644
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:31.335105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8956
33.3%
1 5596
20.8%
0.5 2659
 
9.9%
0.333 1305
 
4.9%
0.667 994
 
3.7%
0.25 796
 
3.0%
0.2 474
 
1.8%
0.4 396
 
1.5%
0.75 376
 
1.4%
0.167 369
 
1.4%
Other values (305) 4960
18.5%
ValueCountFrequency (%)
0 3852
33.4%
1 2408
20.9%
0.5 1148
 
10.0%
0.333 585
 
5.1%
0.667 400
 
3.5%
0.25 337
 
2.9%
0.2 227
 
2.0%
0.4 197
 
1.7%
0.167 165
 
1.4%
0.75 152
 
1.3%
Other values (231) 2050
17.8%
ValueCountFrequency (%)
0 8956
33.3%
0.016 1
 
< 0.1%
0.017 2
 
< 0.1%
0.018 3
 
< 0.1%
0.019 2
 
< 0.1%
0.02 4
 
< 0.1%
0.021 6
 
< 0.1%
0.022 1
 
< 0.1%
0.023 4
 
< 0.1%
0.025 3
 
< 0.1%
ValueCountFrequency (%)
0 3852
33.4%
0.014 1
 
< 0.1%
0.016 1
 
< 0.1%
0.017 1
 
< 0.1%
0.018 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 3
 
< 0.1%
0.022 1
 
< 0.1%
0.023 2
 
< 0.1%
0.024 1
 
< 0.1%
ValueCountFrequency (%)
0 3852
14.3%
0.014 1
 
< 0.1%
0.016 1
 
< 0.1%
0.017 1
 
< 0.1%
0.018 1
 
< 0.1%
0.02 2
 
< 0.1%
0.021 3
 
< 0.1%
0.022 1
 
< 0.1%
0.023 2
 
< 0.1%
0.024 1
 
< 0.1%
ValueCountFrequency (%)
0 8956
77.7%
0.016 1
 
< 0.1%
0.017 2
 
< 0.1%
0.018 3
 
< 0.1%
0.019 2
 
< 0.1%
0.02 4
 
< 0.1%
0.021 6
 
0.1%
0.022 1
 
< 0.1%
0.023 4
 
< 0.1%
0.025 3
 
< 0.1%

pct_tl_closed_L12M
Real number (ℝ)

 TrainTest
Distinct262204
Distinct (%)1.0%1.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.145302180.14503515
 TrainTest
Minimum00
Maximum11
Zeros163106996
Zeros (%)60.7%60.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:31.460982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q30.2220.231
95-th percentile0.6670.667
Maximum11
Range11
Interquartile range (IQR)0.2220.231

Descriptive statistics

 TrainTest
Standard deviation0.247493260.24504452
Coefficient of variation (CV)1.70330041.6895526
Kurtosis3.92934853.8741171
Mean0.145302180.14503515
Median Absolute Deviation (MAD)00
Skewness2.06649552.0415633
Sum3905.8681670.95
Variance0.0612529140.060046815
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:31.591275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16310
60.7%
0.5 1471
 
5.5%
0.333 1246
 
4.6%
1 1166
 
4.3%
0.25 943
 
3.5%
0.2 629
 
2.3%
0.167 537
 
2.0%
0.143 391
 
1.5%
0.125 279
 
1.0%
0.4 270
 
1.0%
Other values (252) 3639
 
13.5%
ValueCountFrequency (%)
0 6996
60.7%
0.5 661
 
5.7%
0.333 533
 
4.6%
1 470
 
4.1%
0.25 389
 
3.4%
0.2 292
 
2.5%
0.167 234
 
2.0%
0.143 171
 
1.5%
0.4 141
 
1.2%
0.667 108
 
0.9%
Other values (194) 1526
 
13.2%
ValueCountFrequency (%)
0 16310
60.7%
0.008 1
 
< 0.1%
0.009 1
 
< 0.1%
0.013 1
 
< 0.1%
0.016 1
 
< 0.1%
0.018 3
 
< 0.1%
0.019 2
 
< 0.1%
0.02 5
 
< 0.1%
0.021 3
 
< 0.1%
0.023 4
 
< 0.1%
ValueCountFrequency (%)
0 6996
60.7%
0.008 1
 
< 0.1%
0.009 1
 
< 0.1%
0.014 1
 
< 0.1%
0.016 2
 
< 0.1%
0.017 2
 
< 0.1%
0.018 1
 
< 0.1%
0.02 1
 
< 0.1%
0.021 2
 
< 0.1%
0.023 2
 
< 0.1%
ValueCountFrequency (%)
0 6996
26.0%
0.008 1
 
< 0.1%
0.009 1
 
< 0.1%
0.014 1
 
< 0.1%
0.016 2
 
< 0.1%
0.017 2
 
< 0.1%
0.018 1
 
< 0.1%
0.02 1
 
< 0.1%
0.021 2
 
< 0.1%
0.023 2
 
< 0.1%
ValueCountFrequency (%)
0 16310
141.6%
0.008 1
 
< 0.1%
0.009 1
 
< 0.1%
0.013 1
 
< 0.1%
0.016 1
 
< 0.1%
0.018 3
 
< 0.1%
0.019 2
 
< 0.1%
0.02 5
 
< 0.1%
0.021 3
 
< 0.1%
0.023 4
 
< 0.1%

Tot_Missed_Pmnt
Real number (ℝ)

 TrainTest
Distinct1916
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.57453220.57139137
 TrainTest
Minimum00
Maximum3434
Zeros175347529
Zeros (%)65.2%65.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:31.690301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile23
Maximum3434
Range3434
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.15184451.1232058
Coefficient of variation (CV)2.00483891.9657381
Kurtosis112.74475112.4823
Mean0.57453220.57139137
Median Absolute Deviation (MAD)00
Skewness6.49526656.2839758
Sum154446583
Variance1.32674571.2615912
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:31.770975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 17534
65.2%
1 6107
 
22.7%
2 1905
 
7.1%
3 757
 
2.8%
4 259
 
1.0%
5 133
 
0.5%
6 79
 
0.3%
7 31
 
0.1%
8 25
 
0.1%
10 13
 
< 0.1%
Other values (9) 38
 
0.1%
ValueCountFrequency (%)
0 7529
65.4%
1 2594
 
22.5%
2 805
 
7.0%
3 343
 
3.0%
4 115
 
1.0%
5 60
 
0.5%
6 35
 
0.3%
7 20
 
0.2%
9 6
 
0.1%
8 6
 
0.1%
Other values (6) 8
 
0.1%
ValueCountFrequency (%)
0 17534
65.2%
1 6107
 
22.7%
2 1905
 
7.1%
3 757
 
2.8%
4 259
 
1.0%
5 133
 
0.5%
6 79
 
0.3%
7 31
 
0.1%
8 25
 
0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
0 7529
65.4%
1 2594
 
22.5%
2 805
 
7.0%
3 343
 
3.0%
4 115
 
1.0%
5 60
 
0.5%
6 35
 
0.3%
7 20
 
0.2%
8 6
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
0 7529
28.0%
1 2594
 
9.6%
2 805
 
3.0%
3 343
 
1.3%
4 115
 
0.4%
5 60
 
0.2%
6 35
 
0.1%
7 20
 
0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
0 17534
152.2%
1 6107
 
53.0%
2 1905
 
16.5%
3 757
 
6.6%
4 259
 
2.2%
5 133
 
1.2%
6 79
 
0.7%
7 31
 
0.3%
8 25
 
0.2%
9 9
 
0.1%

Auto_TL
Real number (ℝ)

 TrainTest
Distinct1612
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.627990030.61262043
 TrainTest
Minimum00
Maximum2323
Zeros151356532
Zeros (%)56.3%56.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:31.850167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile22
Maximum2323
Range2323
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation0.963120170.92468674
Coefficient of variation (CV)1.53365521.5093959
Kurtosis38.44536940.570607
Mean0.627990030.61262043
Median Absolute Deviation (MAD)00
Skewness3.70651483.5477117
Sum168817058
Variance0.927600460.85504557
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:31.924952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 15135
56.3%
1 8553
31.8%
2 2126
 
7.9%
3 635
 
2.4%
4 246
 
0.9%
5 92
 
0.3%
6 45
 
0.2%
7 26
 
0.1%
12 7
 
< 0.1%
8 5
 
< 0.1%
Other values (6) 11
 
< 0.1%
ValueCountFrequency (%)
0 6532
56.7%
1 3654
31.7%
2 909
 
7.9%
3 264
 
2.3%
4 95
 
0.8%
5 35
 
0.3%
6 18
 
0.2%
7 6
 
0.1%
8 4
 
< 0.1%
11 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 15135
56.3%
1 8553
31.8%
2 2126
 
7.9%
3 635
 
2.4%
4 246
 
0.9%
5 92
 
0.3%
6 45
 
0.2%
7 26
 
0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 6532
56.7%
1 3654
31.7%
2 909
 
7.9%
3 264
 
2.3%
4 95
 
0.8%
5 35
 
0.3%
6 18
 
0.2%
7 6
 
0.1%
8 4
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
0 6532
24.3%
1 3654
13.6%
2 909
 
3.4%
3 264
 
1.0%
4 95
 
0.4%
5 35
 
0.1%
6 18
 
0.1%
7 6
 
< 0.1%
8 4
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
0 15135
131.4%
1 8553
74.2%
2 2126
 
18.5%
3 635
 
5.5%
4 246
 
2.1%
5 92
 
0.8%
6 45
 
0.4%
7 26
 
0.2%
8 5
 
< 0.1%
9 1
 
< 0.1%

CC_TL
Real number (ℝ)

 TrainTest
Distinct1412
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.277073030.27367416
 TrainTest
Minimum00
Maximum1427
Zeros218799375
Zeros (%)81.4%81.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:31.997156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile21
Maximum1427
Range1427
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.75396670.75358549
Coefficient of variation (CV)2.7211842.7535866
Kurtosis46.878382158.40753
Mean0.277073030.27367416
Median Absolute Deviation (MAD)00
Skewness5.22506617.4603958
Sum74483153
Variance0.568465780.56789109
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:32.071369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 21879
81.4%
1 3655
 
13.6%
2 821
 
3.1%
3 265
 
1.0%
4 142
 
0.5%
5 50
 
0.2%
6 21
 
0.1%
7 21
 
0.1%
8 11
 
< 0.1%
11 5
 
< 0.1%
Other values (4) 11
 
< 0.1%
ValueCountFrequency (%)
0 9375
81.4%
1 1584
 
13.7%
2 337
 
2.9%
3 121
 
1.1%
4 59
 
0.5%
5 17
 
0.1%
6 14
 
0.1%
7 8
 
0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 21879
81.4%
1 3655
 
13.6%
2 821
 
3.1%
3 265
 
1.0%
4 142
 
0.5%
5 50
 
0.2%
6 21
 
0.1%
7 21
 
0.1%
8 11
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
0 9375
81.4%
1 1584
 
13.7%
2 337
 
2.9%
3 121
 
1.1%
4 59
 
0.5%
5 17
 
0.1%
6 14
 
0.1%
7 8
 
0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 9375
34.9%
1 1584
 
5.9%
2 337
 
1.3%
3 121
 
0.5%
4 59
 
0.2%
5 17
 
0.1%
6 14
 
0.1%
7 8
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 21879
189.9%
1 3655
 
31.7%
2 821
 
7.1%
3 265
 
2.3%
4 142
 
1.2%
5 50
 
0.4%
6 21
 
0.2%
7 21
 
0.2%
8 11
 
0.1%
9 5
 
< 0.1%

Consumer_TL
Real number (ℝ)

 TrainTest
Distinct3733
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.31059111.2607413
 TrainTest
Minimum00
Maximum4139
Zeros143476189
Zeros (%)53.4%53.7%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:32.166580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile66
Maximum4139
Range4139
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation2.69034232.5302464
Coefficient of variation (CV)2.05277012.0069514
Kurtosis43.6833242.581997
Mean1.31059111.2607413
Median Absolute Deviation (MAD)00
Skewness5.21853755.1111997
Sum3523014525
Variance7.23794186.402147
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:32.262674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 14347
53.4%
1 5833
21.7%
2 2517
 
9.4%
3 1426
 
5.3%
4 803
 
3.0%
5 493
 
1.8%
6 402
 
1.5%
7 253
 
0.9%
8 177
 
0.7%
9 150
 
0.6%
Other values (27) 480
 
1.8%
ValueCountFrequency (%)
0 6189
53.7%
1 2479
21.5%
2 1087
 
9.4%
3 615
 
5.3%
4 349
 
3.0%
5 222
 
1.9%
6 183
 
1.6%
7 93
 
0.8%
8 89
 
0.8%
9 40
 
0.3%
Other values (23) 175
 
1.5%
ValueCountFrequency (%)
0 14347
53.4%
1 5833
21.7%
2 2517
 
9.4%
3 1426
 
5.3%
4 803
 
3.0%
5 493
 
1.8%
6 402
 
1.5%
7 253
 
0.9%
8 177
 
0.7%
9 150
 
0.6%
ValueCountFrequency (%)
0 6189
53.7%
1 2479
21.5%
2 1087
 
9.4%
3 615
 
5.3%
4 349
 
3.0%
5 222
 
1.9%
6 183
 
1.6%
7 93
 
0.8%
8 89
 
0.8%
9 40
 
0.3%
ValueCountFrequency (%)
0 6189
23.0%
1 2479
9.2%
2 1087
 
4.0%
3 615
 
2.3%
4 349
 
1.3%
5 222
 
0.8%
6 183
 
0.7%
7 93
 
0.3%
8 89
 
0.3%
9 40
 
0.1%
ValueCountFrequency (%)
0 14347
124.5%
1 5833
50.6%
2 2517
 
21.8%
3 1426
 
12.4%
4 803
 
7.0%
5 493
 
4.3%
6 402
 
3.5%
7 253
 
2.2%
8 177
 
1.5%
9 150
 
1.3%

Gold_TL
Real number (ℝ)

 TrainTest
Distinct7663
Distinct (%)0.3%0.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.59889891.5639267
 TrainTest
Minimum00
Maximum235235
Zeros194618398
Zeros (%)72.4%72.9%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:32.369698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile88
Maximum235235
Range235235
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation5.51250385.6041835
Coefficient of variation (CV)3.44768763.5834054
Kurtosis222.34339344.9942
Mean1.59889891.5639267
Median Absolute Deviation (MAD)00
Skewness10.51302112.863198
Sum4298018018
Variance30.38769831.406872
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:32.484376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19461
72.4%
1 2203
 
8.2%
2 1207
 
4.5%
3 833
 
3.1%
4 559
 
2.1%
5 447
 
1.7%
6 382
 
1.4%
7 257
 
1.0%
8 193
 
0.7%
9 165
 
0.6%
Other values (66) 1174
 
4.4%
ValueCountFrequency (%)
0 8398
72.9%
1 939
 
8.2%
2 503
 
4.4%
3 351
 
3.0%
4 248
 
2.2%
5 178
 
1.5%
6 169
 
1.5%
7 103
 
0.9%
8 82
 
0.7%
9 57
 
0.5%
Other values (53) 493
 
4.3%
ValueCountFrequency (%)
0 19461
72.4%
1 2203
 
8.2%
2 1207
 
4.5%
3 833
 
3.1%
4 559
 
2.1%
5 447
 
1.7%
6 382
 
1.4%
7 257
 
1.0%
8 193
 
0.7%
9 165
 
0.6%
ValueCountFrequency (%)
0 8398
72.9%
1 939
 
8.2%
2 503
 
4.4%
3 351
 
3.0%
4 248
 
2.2%
5 178
 
1.5%
6 169
 
1.5%
7 103
 
0.9%
8 82
 
0.7%
9 57
 
0.5%
ValueCountFrequency (%)
0 8398
31.2%
1 939
 
3.5%
2 503
 
1.9%
3 351
 
1.3%
4 248
 
0.9%
5 178
 
0.7%
6 169
 
0.6%
7 103
 
0.4%
8 82
 
0.3%
9 57
 
0.2%
ValueCountFrequency (%)
0 19461
168.9%
1 2203
 
19.1%
2 1207
 
10.5%
3 833
 
7.2%
4 559
 
4.9%
5 447
 
3.9%
6 382
 
3.3%
7 257
 
2.2%
8 193
 
1.7%
9 165
 
1.4%

Home_TL
Real number (ℝ)

 TrainTest
Distinct78
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0760760390.077076643
 TrainTest
Minimum00
Maximum67
Zeros2537710863
Zeros (%)94.4%94.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:32.565154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile11
Maximum67
Range67
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.359885090.35837644
Coefficient of variation (CV)4.73059724.6496115
Kurtosis59.46389259.78772
Mean0.0760760390.077076643
Median Absolute Deviation (MAD)00
Skewness6.62075036.5201044
Sum2045888
Variance0.129517280.12843368
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:32.941970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 25377
94.4%
1 1126
 
4.2%
2 280
 
1.0%
3 54
 
0.2%
4 29
 
0.1%
5 9
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
0 10863
94.3%
1 490
 
4.3%
2 131
 
1.1%
3 21
 
0.2%
4 10
 
0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 25377
94.4%
1 1126
 
4.2%
2 280
 
1.0%
3 54
 
0.2%
4 29
 
0.1%
5 9
 
< 0.1%
6 6
 
< 0.1%
ValueCountFrequency (%)
0 10863
94.3%
1 490
 
4.3%
2 131
 
1.1%
3 21
 
0.2%
4 10
 
0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 10863
40.4%
1 490
 
1.8%
2 131
 
0.5%
3 21
 
0.1%
4 10
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 25377
220.3%
1 1126
 
9.8%
2 280
 
2.4%
3 54
 
0.5%
4 29
 
0.3%
5 9
 
0.1%
6 6
 
0.1%

PL_TL
Real number (ℝ)

 TrainTest
Distinct1714
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.342918790.33252322
 TrainTest
Minimum00
Maximum2321
Zeros216629343
Zeros (%)80.6%81.1%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:33.018263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile22
Maximum2321
Range2321
Interquartile range (IQR)00

Descriptive statistics

 TrainTest
Standard deviation0.950778480.91964967
Coefficient of variation (CV)2.77260542.7656705
Kurtosis56.0480954.796859
Mean0.342918790.33252322
Median Absolute Deviation (MAD)00
Skewness5.48422485.3961008
Sum92183831
Variance0.903979710.84575552
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:33.095693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 21662
80.6%
1 3278
 
12.2%
2 1016
 
3.8%
3 452
 
1.7%
4 213
 
0.8%
5 113
 
0.4%
7 49
 
0.2%
6 48
 
0.2%
8 25
 
0.1%
9 7
 
< 0.1%
Other values (7) 18
 
0.1%
ValueCountFrequency (%)
0 9343
81.1%
1 1333
 
11.6%
2 456
 
4.0%
3 207
 
1.8%
4 88
 
0.8%
5 42
 
0.4%
6 22
 
0.2%
7 13
 
0.1%
8 8
 
0.1%
12 5
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 21662
80.6%
1 3278
 
12.2%
2 1016
 
3.8%
3 452
 
1.7%
4 213
 
0.8%
5 113
 
0.4%
6 48
 
0.2%
7 49
 
0.2%
8 25
 
0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
0 9343
81.1%
1 1333
 
11.6%
2 456
 
4.0%
3 207
 
1.8%
4 88
 
0.8%
5 42
 
0.4%
6 22
 
0.2%
7 13
 
0.1%
8 8
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 9343
34.8%
1 1333
 
5.0%
2 456
 
1.7%
3 207
 
0.8%
4 88
 
0.3%
5 42
 
0.2%
6 22
 
0.1%
7 13
 
< 0.1%
8 8
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 21662
188.0%
1 3278
 
28.5%
2 1016
 
8.8%
3 452
 
3.9%
4 213
 
1.8%
5 113
 
1.0%
6 48
 
0.4%
7 49
 
0.4%
8 25
 
0.2%
9 7
 
0.1%

Secured_TL
Real number (ℝ)

 TrainTest
Distinct8071
Distinct (%)0.3%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.90111982.8782224
 TrainTest
Minimum00
Maximum235235
Zeros75853270
Zeros (%)28.2%28.4%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:33.195687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q333
95-th percentile1212
Maximum235235
Range235235
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation6.26493216.4144757
Coefficient of variation (CV)2.15948762.2286241
Kurtosis142.63846207.14896
Mean2.90111982.8782224
Median Absolute Deviation (MAD)11
Skewness8.25816439.6912883
Sum7798533160
Variance39.24937441.145499
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:33.310946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8605
32.0%
0 7585
28.2%
2 3522
13.1%
3 1767
 
6.6%
4 1108
 
4.1%
5 768
 
2.9%
6 629
 
2.3%
7 433
 
1.6%
8 375
 
1.4%
9 245
 
0.9%
Other values (70) 1844
 
6.9%
ValueCountFrequency (%)
1 3657
31.7%
0 3270
28.4%
2 1531
13.3%
3 827
 
7.2%
4 458
 
4.0%
5 329
 
2.9%
6 265
 
2.3%
7 174
 
1.5%
8 152
 
1.3%
9 103
 
0.9%
Other values (61) 755
 
6.6%
ValueCountFrequency (%)
0 7585
28.2%
1 8605
32.0%
2 3522
13.1%
3 1767
 
6.6%
4 1108
 
4.1%
5 768
 
2.9%
6 629
 
2.3%
7 433
 
1.6%
8 375
 
1.4%
9 245
 
0.9%
ValueCountFrequency (%)
0 3270
28.4%
1 3657
31.7%
2 1531
13.3%
3 827
 
7.2%
4 458
 
4.0%
5 329
 
2.9%
6 265
 
2.3%
7 174
 
1.5%
8 152
 
1.3%
9 103
 
0.9%
ValueCountFrequency (%)
0 3270
12.2%
1 3657
13.6%
2 1531
5.7%
3 827
 
3.1%
4 458
 
1.7%
5 329
 
1.2%
6 265
 
1.0%
7 174
 
0.6%
8 152
 
0.6%
9 103
 
0.4%
ValueCountFrequency (%)
0 7585
65.8%
1 8605
74.7%
2 3522
30.6%
3 1767
 
15.3%
4 1108
 
9.6%
5 768
 
6.7%
6 629
 
5.5%
7 433
 
3.8%
8 375
 
3.3%
9 245
 
2.1%

Unsecured_TL
Real number (ℝ)

 TrainTest
Distinct4740
Distinct (%)0.2%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.42293812.3653329
 TrainTest
Minimum00
Maximum5546
Zeros84543604
Zeros (%)31.4%31.3%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:33.415947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median11
Q333
95-th percentile99
Maximum5546
Range5546
Interquartile range (IQR)33

Descriptive statistics

 TrainTest
Standard deviation3.83610773.67968
Coefficient of variation (CV)1.58324621.5556711
Kurtosis24.86853822.157173
Mean2.42293812.3653329
Median Absolute Deviation (MAD)11
Skewness3.93135583.7775792
Sum6513127251
Variance14.71572213.540045
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:33.522271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 8454
31.4%
1 7065
26.3%
2 3601
13.4%
3 2150
 
8.0%
4 1430
 
5.3%
5 942
 
3.5%
6 663
 
2.5%
7 525
 
2.0%
8 423
 
1.6%
9 289
 
1.1%
Other values (37) 1339
 
5.0%
ValueCountFrequency (%)
0 3604
31.3%
1 3060
26.6%
2 1572
13.6%
3 910
 
7.9%
4 611
 
5.3%
5 437
 
3.8%
6 304
 
2.6%
7 194
 
1.7%
8 169
 
1.5%
9 125
 
1.1%
Other values (30) 535
 
4.6%
ValueCountFrequency (%)
0 8454
31.4%
1 7065
26.3%
2 3601
13.4%
3 2150
 
8.0%
4 1430
 
5.3%
5 942
 
3.5%
6 663
 
2.5%
7 525
 
2.0%
8 423
 
1.6%
9 289
 
1.1%
ValueCountFrequency (%)
0 3604
31.3%
1 3060
26.6%
2 1572
13.6%
3 910
 
7.9%
4 611
 
5.3%
5 437
 
3.8%
6 304
 
2.6%
7 194
 
1.7%
8 169
 
1.5%
9 125
 
1.1%
ValueCountFrequency (%)
0 3604
13.4%
1 3060
11.4%
2 1572
5.8%
3 910
 
3.4%
4 611
 
2.3%
5 437
 
1.6%
6 304
 
1.1%
7 194
 
0.7%
8 169
 
0.6%
9 125
 
0.5%
ValueCountFrequency (%)
0 8454
73.4%
1 7065
61.3%
2 3601
31.3%
3 2150
 
18.7%
4 1430
 
12.4%
5 942
 
8.2%
6 663
 
5.8%
7 525
 
4.6%
8 423
 
3.7%
9 289
 
2.5%

Other_TL
Real number (ℝ)

 TrainTest
Distinct3732
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.090511.1229928
 TrainTest
Minimum00
Maximum8065
Zeros151276457
Zeros (%)56.3%56.0%
Negative00
Negative (%)0.0%0.0%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:33.625773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q100
median00
Q311
95-th percentile55
Maximum8065
Range8065
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation2.32918522.4523359
Coefficient of variation (CV)2.13586782.1837503
Kurtosis88.36901475.473004
Mean1.090511.1229928
Median Absolute Deviation (MAD)00
Skewness6.25560216.2254186
Sum2931412938
Variance5.42510386.0139513
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:33.729177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 15127
56.3%
1 6171
23.0%
2 2329
 
8.7%
3 1120
 
4.2%
4 675
 
2.5%
5 389
 
1.4%
6 276
 
1.0%
7 159
 
0.6%
8 141
 
0.5%
9 100
 
0.4%
Other values (27) 394
 
1.5%
ValueCountFrequency (%)
0 6457
56.0%
1 2623
22.8%
2 1013
 
8.8%
3 495
 
4.3%
4 295
 
2.6%
5 176
 
1.5%
6 108
 
0.9%
7 72
 
0.6%
8 51
 
0.4%
9 48
 
0.4%
Other values (22) 183
 
1.6%
ValueCountFrequency (%)
0 15127
56.3%
1 6171
23.0%
2 2329
 
8.7%
3 1120
 
4.2%
4 675
 
2.5%
5 389
 
1.4%
6 276
 
1.0%
7 159
 
0.6%
8 141
 
0.5%
9 100
 
0.4%
ValueCountFrequency (%)
0 6457
56.0%
1 2623
22.8%
2 1013
 
8.8%
3 495
 
4.3%
4 295
 
2.6%
5 176
 
1.5%
6 108
 
0.9%
7 72
 
0.6%
8 51
 
0.4%
9 48
 
0.4%
ValueCountFrequency (%)
0 6457
24.0%
1 2623
9.8%
2 1013
 
3.8%
3 495
 
1.8%
4 295
 
1.1%
5 176
 
0.7%
6 108
 
0.4%
7 72
 
0.3%
8 51
 
0.2%
9 48
 
0.2%
ValueCountFrequency (%)
0 15127
131.3%
1 6171
53.6%
2 2329
 
20.2%
3 1120
 
9.7%
4 675
 
5.9%
5 389
 
3.4%
6 276
 
2.4%
7 159
 
1.4%
8 141
 
1.2%
9 100
 
0.9%

Age_Oldest_TL
Real number (ℝ)

 TrainTest
Distinct261242
Distinct (%)1.0%2.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean46.98370646.856002
 TrainTest
Minimum-1-1
Maximum370359
Zeros83
Zeros (%)< 0.1%< 0.1%
Negative1713
Negative (%)0.1%0.1%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:33.841095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile55
Q11414
median3535
Q36666
95-th percentile139138
Maximum370359
Range371360
Interquartile range (IQR)5252

Descriptive statistics

 TrainTest
Standard deviation42.84805842.73204
Coefficient of variation (CV)0.911976970.91198648
Kurtosis3.11357913.42299
Mean46.98370646.856002
Median Absolute Deviation (MAD)2323
Skewness1.58473761.6166288
Sum1262969539828
Variance1835.95611826.0273
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:33.960443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 729
 
2.7%
10 665
 
2.5%
8 654
 
2.4%
9 570
 
2.1%
6 564
 
2.1%
11 554
 
2.1%
14 547
 
2.0%
12 533
 
2.0%
15 508
 
1.9%
13 508
 
1.9%
Other values (251) 21049
78.3%
ValueCountFrequency (%)
7 318
 
2.8%
8 274
 
2.4%
9 269
 
2.3%
11 256
 
2.2%
10 247
 
2.1%
6 242
 
2.1%
13 238
 
2.1%
12 233
 
2.0%
16 221
 
1.9%
14 212
 
1.8%
Other values (232) 9011
78.2%
ValueCountFrequency (%)
-1 17
 
0.1%
0 8
 
< 0.1%
1 84
 
0.3%
2 312
1.2%
3 351
1.3%
4 373
1.4%
5 377
1.4%
6 564
2.1%
7 729
2.7%
8 654
2.4%
ValueCountFrequency (%)
-1 13
 
0.1%
0 3
 
< 0.1%
1 36
 
0.3%
2 121
 
1.1%
3 155
1.3%
4 160
1.4%
5 177
1.5%
6 242
2.1%
7 318
2.8%
8 274
2.4%
ValueCountFrequency (%)
-1 13
 
< 0.1%
0 3
 
< 0.1%
1 36
 
0.1%
2 121
 
0.5%
3 155
0.6%
4 160
0.6%
5 177
0.7%
6 242
0.9%
7 318
1.2%
8 274
1.0%
ValueCountFrequency (%)
-1 17
 
0.1%
0 8
 
0.1%
1 84
 
0.7%
2 312
2.7%
3 351
3.0%
4 373
3.2%
5 377
3.3%
6 564
4.9%
7 729
6.3%
8 654
5.7%

Age_Newest_TL
Real number (ℝ)

 TrainTest
Distinct182165
Distinct (%)0.7%1.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean14.98169715.050082
 TrainTest
Minimum-1-1
Maximum370359
Zeros7436
Zeros (%)0.3%0.3%
Negative1713
Negative (%)0.1%0.1%
Memory size420.0 KiB180.0 KiB
2025-03-10T23:50:34.076528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum-1-1
5-th percentile22
Q144
median78
Q31616
95-th percentile5756
Maximum370359
Range371360
Interquartile range (IQR)1212

Descriptive statistics

 TrainTest
Standard deviation21.40659821.540619
Coefficient of variation (CV)1.428851.4312625
Kurtosis22.71115926.083457
Mean14.98169715.050082
Median Absolute Deviation (MAD)45
Skewness3.77817753.9560486
Sum402723173392
Variance458.24245463.99827
MonotonicityNot monotonicNot monotonic
2025-03-10T23:50:34.201152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2910
 
10.8%
3 2597
 
9.7%
4 2152
 
8.0%
5 1723
 
6.4%
6 1651
 
6.1%
7 1415
 
5.3%
8 1307
 
4.9%
1 978
 
3.6%
10 969
 
3.6%
9 914
 
3.4%
Other values (172) 10265
38.2%
ValueCountFrequency (%)
2 1211
 
10.5%
3 1067
 
9.3%
4 930
 
8.1%
5 752
 
6.5%
6 717
 
6.2%
7 581
 
5.0%
8 559
 
4.9%
9 468
 
4.1%
1 410
 
3.6%
10 399
 
3.5%
Other values (155) 4427
38.4%
ValueCountFrequency (%)
-1 17
 
0.1%
0 74
 
0.3%
1 978
 
3.6%
2 2910
10.8%
3 2597
9.7%
4 2152
8.0%
5 1723
6.4%
6 1651
6.1%
7 1415
5.3%
8 1307
4.9%
ValueCountFrequency (%)
-1 13
 
0.1%
0 36
 
0.3%
1 410
 
3.6%
2 1211
10.5%
3 1067
9.3%
4 930
8.1%
5 752
6.5%
6 717
6.2%
7 581
5.0%
8 559
4.9%
ValueCountFrequency (%)
-1 13
 
< 0.1%
0 36
 
0.1%
1 410
 
1.5%
2 1211
4.5%
3 1067
4.0%
4 930
3.5%
5 752
2.8%
6 717
2.7%
7 581
2.2%
8 559
2.1%
ValueCountFrequency (%)
-1 17
 
0.1%
0 74
 
0.6%
1 978
 
8.5%
2 2910
25.3%
3 2597
22.5%
4 2152
18.7%
5 1723
15.0%
6 1651
14.3%
7 1415
12.3%
8 1307
11.3%

Correlations

Train

2025-03-10T23:50:34.357724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Test

2025-03-10T23:50:34.819456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Train

AGEAge_Newest_TLAge_Oldest_TLAuto_TLCC_FlagCC_TLCC_enqCC_enq_L12mCC_enq_L6mCC_utilizationConsumer_TLCredit_ScoreEDUCATIONGENDERGL_FlagGold_TLHL_FlagHome_TLMARITALSTATUSNETMONTHLYINCOMEOther_TLPL_FlagPL_TLPL_enqPL_enq_L12mPL_enq_L6mPL_utilizationPROSPECTIDSecured_TLTime_With_Curr_EmprTot_Active_TLTot_Closed_TLTot_Missed_PmntTot_TL_closed_L12MTot_TL_closed_L6MTotal_TLTotal_TL_opened_L12MTotal_TL_opened_L6MUnsecured_TLenq_L12menq_L3menq_L6mfirst_prod_enq2last_prod_enq2max_delinquency_levelmax_deliq_12mtsmax_deliq_6mtsmax_recent_level_of_deliqmax_unsec_exposure_inPctnum_dbtnum_dbt_12mtsnum_dbt_6mtsnum_deliq_12mtsnum_deliq_6_12mtsnum_deliq_6mtsnum_lssnum_lss_12mtsnum_lss_6mtsnum_stdnum_std_12mtsnum_std_6mtsnum_subnum_sub_12mtsnum_sub_6mtsnum_times_30p_dpdnum_times_60p_dpdnum_times_delinquentpct_CC_enq_L6m_of_L12mpct_CC_enq_L6m_of_everpct_PL_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_active_tlpct_closed_tlpct_currentBal_all_TLpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_tl_closed_L12Mpct_tl_closed_L6Mpct_tl_open_L12Mpct_tl_open_L6Mrecent_level_of_deliqresponse_flagtime_since_first_deliquencytime_since_recent_deliquencytime_since_recent_enqtime_since_recent_paymenttot_enq
AGE1.0000.1090.3520.0370.044-0.020-0.067-0.096-0.087-0.0390.0060.2690.0570.1030.1430.0940.1080.1340.5810.1520.1420.1020.097-0.024-0.098-0.1030.062-0.0010.1620.4280.0410.2230.0130.0540.0210.169-0.064-0.0760.065-0.141-0.109-0.1360.0660.0420.073-0.032-0.0450.0730.0400.0340.0090.0000.0290.0270.0360.0090.0030.0140.1590.1220.1070.0270.0060.0120.0720.0680.075-0.091-0.093-0.103-0.112-0.1910.191-0.052-0.191-0.0900.0140.001-0.205-0.1330.0690.0320.0780.0640.0620.099-0.028
Age_Newest_TL0.1091.0000.215-0.0190.118-0.212-0.279-0.305-0.295-0.218-0.4200.1650.0170.0000.027-0.1220.088-0.0160.065-0.084-0.2130.110-0.219-0.341-0.361-0.342-0.2400.002-0.1070.089-0.614-0.123-0.521-0.306-0.316-0.439-0.825-0.846-0.461-0.502-0.367-0.4680.0560.074-0.009-0.299-0.390-0.008-0.4270.0180.0190.075-0.036-0.001-0.0560.0370.0250.014-0.070-0.124-0.1450.0340.0210.0030.0010.002-0.011-0.254-0.255-0.302-0.302-0.2480.248-0.502-0.248-0.815-0.203-0.264-0.683-0.815-0.0020.037-0.004-0.0010.1410.397-0.409
Age_Oldest_TL0.3520.2151.0000.3120.1520.1480.0800.005-0.0020.1150.0100.4790.0390.0560.2460.3150.2800.1680.2140.1460.3200.1880.2000.069-0.045-0.0470.127-0.0000.5280.2010.1940.6440.0650.2120.1240.531-0.089-0.0740.187-0.104-0.062-0.0800.1010.0520.297-0.029-0.0840.2930.1150.0760.0290.0260.1210.1270.0940.0510.0240.0130.3590.2420.2030.1010.0370.0140.2720.2440.2990.0120.007-0.054-0.070-0.5320.532-0.098-0.532-0.1460.0940.059-0.579-0.2630.2810.0190.3150.2870.0750.2090.138
Auto_TL0.037-0.0190.3121.0000.1430.0910.1490.1370.1430.083-0.0120.1230.0140.0480.073-0.0120.0660.0190.0610.085-0.0280.0930.0460.0980.0810.0990.0330.0010.4470.0560.2010.1870.0730.0930.0680.2190.0730.042-0.0200.1030.0800.0900.0720.0320.1670.1480.1530.173-0.039-0.015-0.0080.0000.1160.0990.106-0.021-0.0070.0000.0660.0520.047-0.004-0.015-0.0050.1140.0710.1820.0490.0470.0160.011-0.0700.0700.023-0.070-0.0110.0270.030-0.120-0.0460.1670.0160.1840.1640.136-0.0830.246
CC_Flag0.0440.1180.1520.1431.0000.4800.4540.4400.4710.9490.2270.1120.2060.0650.0860.0240.0000.1020.0250.0840.0300.2480.1720.2980.1830.1780.2461.0000.0290.0430.3270.0620.0490.1600.1740.1200.2290.2000.3470.2780.1630.2190.4550.4180.0190.0470.0510.0250.0290.0000.0000.0000.1490.1220.1500.0000.0000.0000.0410.0340.0410.0120.0000.0000.0410.0420.0960.4780.4800.2040.2440.2660.2570.0160.2660.2330.1890.1690.2200.1980.0280.1770.1170.0870.0880.1340.349
CC_TL-0.020-0.2120.1480.0910.4801.0000.6760.5160.4200.9420.149-0.0160.0610.0320.1420.0080.0160.0940.0260.1760.0310.2150.2690.3120.2420.2130.2490.0030.034-0.0490.3860.1540.1140.1470.1440.3070.2570.2090.4260.2990.2010.2500.1300.0900.1110.2320.2660.1040.408-0.011-0.0080.0000.1580.1330.162-0.013-0.0100.0000.0130.0090.011-0.030-0.018-0.0120.0660.0350.1320.4630.4580.1480.1350.066-0.0660.1320.0660.1330.0530.0910.0560.1200.0940.1180.1200.0950.013-0.2670.401
CC_enq-0.067-0.2790.0800.1490.4540.6761.0000.8990.8310.6510.307-0.1570.0590.0500.071-0.0460.0220.0740.0380.2050.0210.2320.3100.6150.5800.5730.2840.006-0.009-0.0830.4000.1490.1190.1890.1900.3130.3190.2650.4810.6110.5310.5720.1230.1220.1250.3050.3380.1230.423-0.020-0.0080.0000.1670.1420.152-0.006-0.0060.000-0.037-0.027-0.020-0.010-0.008-0.0060.0690.0330.1450.6010.5990.2930.2850.078-0.0780.1000.0780.1860.1060.1440.1360.1850.1150.2150.1320.1110.244-0.2300.683
CC_enq_L12m-0.096-0.3050.0050.1370.4400.5160.8991.0000.9210.5170.313-0.2140.0570.0470.028-0.0700.0000.0400.0760.1620.0130.2060.2620.6090.6100.6110.2480.007-0.037-0.0980.3530.1020.1200.1770.1830.2590.3450.2890.4250.6670.5820.6260.0960.1700.0840.2740.3080.0850.369-0.018-0.0050.0000.1270.0990.114-0.002-0.0030.000-0.060-0.045-0.037-0.006-0.004-0.0030.0340.0080.0970.6850.6860.3100.3060.094-0.0940.0900.0940.2080.1110.1450.2060.2230.0810.2620.0860.0760.248-0.2010.657
CC_enq_L6m-0.087-0.295-0.0020.1430.4710.4200.8310.9211.0000.4210.312-0.2240.0600.0490.030-0.0800.0200.0310.0860.1530.0080.2100.2350.6050.6150.6300.2220.004-0.040-0.0880.3220.0870.1200.1610.1700.2330.3110.2940.3920.6460.6120.6530.1040.2190.0760.2600.2900.0780.333-0.020-0.0040.0000.1090.0860.092-0.003-0.0000.000-0.064-0.049-0.041-0.0010.002-0.0000.0300.0060.0860.7590.7590.3080.3050.089-0.0890.0740.0890.2270.1040.1370.1870.2400.0750.2850.0790.0700.280-0.1770.636
CC_utilization-0.039-0.2180.1150.0830.9490.9420.6510.5170.4211.0000.150-0.0400.0850.0680.0790.0080.0000.0690.0510.1610.0310.2510.2560.3100.2480.2200.2430.0010.025-0.0560.3770.1320.1190.1380.1390.2880.2620.2140.4040.3060.2070.2580.1950.1830.0980.2230.2560.0930.409-0.009-0.0070.0000.1500.1250.152-0.012-0.0100.0000.0030.0030.005-0.030-0.018-0.0110.0510.0170.1180.4690.4660.1590.1480.081-0.0810.1560.0810.1370.0480.0870.0740.1300.0850.2130.1080.0860.002-0.2690.392
Consumer_TL0.006-0.4200.010-0.0120.2270.1490.3070.3130.3120.1501.000-0.1450.0230.0240.025-0.0790.026-0.0160.0350.0970.0990.2460.1900.4440.4080.3630.1980.008-0.149-0.0370.4740.2670.1970.3900.3900.4380.5150.4130.7600.4560.3190.3890.0710.0690.0540.3090.3500.0570.496-0.027-0.0200.0000.0940.0820.065-0.026-0.0070.000-0.123-0.124-0.119-0.032-0.014-0.0090.001-0.0150.0630.1740.1740.2670.261-0.0130.013-0.061-0.0130.2690.3130.3460.2610.2950.0560.0840.0540.0540.028-0.1380.521
Credit_Score0.2690.1650.4790.1230.112-0.016-0.157-0.214-0.224-0.040-0.1451.0000.0360.0180.1920.1620.1620.1290.1580.0370.1930.1290.036-0.228-0.334-0.374-0.009-0.0280.2850.1850.0470.2570.0090.0690.0180.184-0.097-0.152-0.034-0.450-0.513-0.4900.0890.118-0.202-0.172-0.150-0.199-0.0200.0380.0030.000-0.089-0.088-0.0300.015-0.0010.0000.4020.3830.3640.0490.0130.006-0.124-0.083-0.187-0.192-0.195-0.385-0.393-0.2080.2080.030-0.208-0.1600.019-0.007-0.274-0.224-0.2110.175-0.203-0.2300.2980.103-0.274
EDUCATION0.0570.0170.0390.0140.2060.0610.0590.0570.0600.0850.0230.0361.0000.0560.0850.0130.0630.0400.1600.0270.0160.1170.0450.0640.0420.0380.0441.0000.0130.0420.0380.0150.0090.0200.0280.0170.0250.0330.0460.0430.0360.0380.0740.0660.0250.0300.0280.0220.0600.0030.0000.0050.0230.0130.0290.0150.0000.0090.0250.0270.0290.0100.0000.0000.0050.0090.0120.0620.0610.0460.0490.0410.0390.0030.0410.0290.0300.0220.0340.0270.0180.0520.0170.0140.0180.0220.047
GENDER0.1030.0000.0560.0480.0650.0320.0500.0470.0490.0680.0240.0180.0561.0000.0000.0320.0000.0000.1320.0210.0090.0190.0000.0430.0360.0460.0161.0000.0180.0520.0290.0220.0210.0100.0140.0210.0180.0270.0310.0390.0000.0310.0770.0500.0290.0020.0000.0140.0000.0000.0000.0000.0090.0050.0260.0000.0000.0000.0220.0240.0260.0000.0000.0000.0300.0280.0370.0490.0500.0420.0400.0360.0360.0000.0360.0350.0320.0250.0400.0260.0180.0160.0400.0240.0170.0000.037
GL_Flag0.1430.0270.2460.0730.0860.1420.0710.0280.0300.0790.0250.1920.0850.0001.0000.0190.0121.0000.0750.0500.0560.0830.1040.0680.0250.0440.0641.0000.0530.1010.1280.0260.0370.0250.0340.0580.0470.0430.0760.0180.0410.0290.4220.2780.0300.0430.0500.0220.0000.0000.0240.0020.1070.1000.0890.0000.0000.0000.1630.1550.1480.0000.0000.0000.0550.0430.1050.0320.0440.0230.0420.1140.1140.0000.1140.0270.0850.0610.1160.0830.0230.0080.1170.1060.0100.0410.087
Gold_TL0.094-0.1220.315-0.0120.0240.008-0.046-0.070-0.0800.008-0.0790.1620.0130.0320.0191.0000.1670.0180.0360.0370.1380.0280.026-0.031-0.062-0.0700.0160.0050.6720.0210.1970.5490.2340.2500.1520.4970.1690.128-0.030-0.043-0.034-0.0320.0170.0140.220-0.025-0.0560.212-0.0230.0200.0020.0000.0400.0330.028-0.0110.0040.0440.3360.2860.2630.018-0.011-0.0090.2370.2260.206-0.004-0.005-0.021-0.026-0.3710.3710.162-0.3710.0720.1300.092-0.1440.0040.1970.0090.2300.218-0.1040.046-0.018
HL_Flag0.1080.0880.2800.0660.0000.0160.0220.0000.0200.0000.0260.1620.0630.0000.0120.1671.0000.0100.0880.0210.1160.0150.0200.0370.0000.0200.0291.0000.1980.0120.1760.2010.1460.1390.1180.2200.1400.1130.0550.0140.0230.0190.0500.0510.1480.0360.0210.0920.0230.0000.0000.0000.0430.0430.0390.0200.0130.0160.2490.2640.2420.0140.0150.0000.1030.1040.1250.0280.0330.0360.0590.4460.4530.0000.4460.1460.2200.1430.3200.2060.0490.0000.2360.2100.0430.0720.040
Home_TL0.134-0.0160.1680.0190.1020.0940.0740.0400.0310.069-0.0160.1290.0400.0001.0000.0180.0101.0000.0760.1090.0490.0990.0910.0530.0050.0070.046-0.0130.1900.0990.1540.0870.0750.0320.0100.1370.0320.0120.0490.0110.0000.0060.1940.1260.1190.1050.0960.1180.0360.0080.0070.0000.1150.1080.110-0.004-0.0050.0000.1470.1470.1420.000-0.004-0.0020.0990.0710.1240.0250.024-0.005-0.0110.009-0.0090.1420.0090.000-0.009-0.010-0.058-0.0240.1120.0080.1150.0970.029-0.0480.095
MARITALSTATUS0.5810.0650.2140.0610.0250.0260.0380.0760.0860.0510.0350.1580.1600.1320.0750.0360.0880.0761.0000.0180.0470.0570.0420.0060.0540.0660.0571.0000.0410.2510.0450.0500.0220.0250.0230.0550.0090.0060.0480.0710.0680.0750.0820.0750.0640.0120.0290.0430.0140.0000.0150.0110.0470.0440.0390.0100.0000.0000.0960.0840.0740.0060.0000.0050.0510.0450.0710.0890.0800.0720.0920.1500.1530.0150.1500.0550.0670.0480.1710.1320.0350.0310.0880.0540.0650.0630.001
NETMONTHLYINCOME0.152-0.0840.1460.0850.0840.1760.2050.1620.1530.1610.0970.0370.0270.0210.0500.0370.0210.1090.0181.0000.0660.0380.1510.1710.1230.1150.1240.0040.0870.2660.1770.1280.0780.0750.0710.1830.1110.0920.1780.1420.1040.1260.0300.0210.0780.0850.0970.0790.0750.0140.0110.0000.0530.0530.049-0.0010.0070.0000.0580.0540.0520.0070.0090.0130.0650.0600.0840.1280.1270.0770.072-0.0210.0210.057-0.0210.0540.0280.047-0.0060.0440.0720.0140.0850.0710.034-0.0670.218
Other_TL0.142-0.2130.320-0.0280.0300.0310.0210.0130.0080.0310.0990.1930.0160.0090.0560.1380.1160.0490.0470.0661.0000.0280.0740.0730.0380.0290.049-0.0040.3470.0720.4320.3650.3350.2800.2330.4900.3080.2610.3110.0720.0430.0670.0230.0250.1600.0990.0830.1570.2540.0670.0240.0000.1000.0890.0810.0400.0120.0000.3890.3370.3020.0980.0550.0380.1520.1450.1590.0380.0370.0390.035-0.1090.1090.227-0.1090.1540.1530.165-0.0300.1240.1490.0050.1530.139-0.061-0.0560.133
PL_Flag0.1020.1100.1880.0930.2480.2150.2320.2060.2100.2510.2460.1290.1170.0190.0830.0280.0150.0990.0570.0380.0281.0000.3840.4410.2720.2920.8831.0000.0330.0380.3120.0740.0620.1980.2290.1270.2670.2530.3860.2350.1400.1990.2830.2680.0100.0200.0300.0060.0350.0000.0000.0000.0860.0780.0730.0000.0000.0070.1210.1020.1080.0110.0000.0110.0210.0270.0880.2110.2230.3270.3730.3070.3110.0000.3070.2540.2510.2210.2580.2260.0060.1400.0980.0730.0800.1030.297
PL_TL0.097-0.2190.2000.0460.1720.2690.3100.2620.2350.2560.1900.0360.0450.0000.1040.0260.0200.0910.0420.1510.0740.3841.0000.5300.3690.3080.8780.0030.0450.0350.3600.2810.1640.2590.2350.3670.2690.2430.4880.2640.1820.2310.0880.0530.0960.1420.1500.0980.505-0.012-0.0080.0000.1070.0900.098-0.007-0.0070.0000.1620.1590.156-0.010-0.018-0.0060.0590.0360.1090.2090.2050.2650.247-0.0620.0620.139-0.0620.1600.1670.1820.0280.1400.0910.0930.1030.0860.005-0.1390.386
PL_enq-0.024-0.3410.0690.0980.2980.3120.6150.6090.6050.3100.444-0.2280.0640.0430.068-0.0310.0370.0530.0060.1710.0730.4410.5301.0000.8960.8140.4860.007-0.022-0.0500.4250.2130.1630.2630.2630.3700.3940.3390.5600.6810.5570.6330.1070.1090.1070.2700.3020.1100.501-0.0110.0000.0000.1280.1070.115-0.0060.0110.0000.0080.0240.027-0.008-0.014-0.0040.0600.0370.1200.3250.3240.6430.6350.035-0.0350.0960.0350.2420.1750.2150.1890.2490.1040.2510.1110.0960.156-0.1700.746
PL_enq_L12m-0.098-0.361-0.0450.0810.1830.2420.5800.6100.6150.2480.408-0.3340.0420.0360.025-0.0620.0000.0050.0540.1230.0380.2720.3690.8961.0000.9010.3720.010-0.066-0.0940.3620.1050.1420.2090.2230.2700.4070.3530.4580.7430.6060.6890.0660.0840.0630.2490.2860.0650.414-0.011-0.0040.0000.0900.0720.079-0.0090.0040.000-0.065-0.043-0.032-0.010-0.011-0.0030.0320.0150.0720.3160.3150.7430.7450.098-0.0980.0960.0980.2650.1500.1900.2750.2930.0610.2860.0650.0560.136-0.1590.684
PL_enq_L6m-0.103-0.342-0.0470.0990.1780.2130.5730.6110.6300.2200.363-0.3740.0380.0460.044-0.0700.0200.0070.0660.1150.0290.2920.3080.8140.9011.0000.3040.015-0.060-0.0970.3190.0810.1390.1750.1970.2330.3530.3580.4040.7090.6510.7380.0710.1360.0650.2370.2680.0660.346-0.010-0.0010.0000.0890.0720.076-0.0040.0090.000-0.071-0.048-0.037-0.002-0.0030.0020.0350.0160.0720.3040.3040.8580.8580.091-0.0910.0830.0910.2900.1280.1710.2390.3120.0630.2870.0660.0560.144-0.1500.656
PL_utilization0.062-0.2400.1270.0330.2460.2490.2840.2480.2220.2430.198-0.0090.0440.0160.0640.0160.0290.0460.0570.1240.0490.8830.8780.4860.3720.3041.0000.0060.0160.0120.3740.1980.1700.2050.1890.3220.2820.2520.4390.2660.1770.2300.1060.1170.0680.1350.1520.0710.547-0.017-0.0060.0000.0840.0630.079-0.008-0.0090.0000.1080.1160.117-0.019-0.020-0.0130.0310.0110.0790.1990.1960.2710.2540.019-0.0190.1740.0190.1720.1190.1390.0680.1600.0660.1690.0760.063-0.003-0.1580.352
PROSPECTID-0.0010.002-0.0000.0011.0000.0030.0060.0070.0040.0010.008-0.0281.0001.0001.0000.0051.000-0.0131.0000.004-0.0041.0000.0030.0070.0100.0150.0061.0000.0010.0070.001-0.0010.0010.002-0.0030.003-0.0010.0030.0040.0150.0220.0171.0001.0000.0150.000-0.0110.0140.0030.0030.0031.0000.0040.014-0.010-0.007-0.0161.000-0.010-0.007-0.0060.0000.000-0.0010.0090.0120.0120.0020.0020.0160.0170.000-0.000-0.0100.0000.002-0.001-0.005-0.0040.0030.0151.0000.0140.018-0.011-0.0020.011
Secured_TL0.162-0.1070.5280.4470.0290.034-0.009-0.037-0.0400.025-0.1490.2850.0130.0180.0530.6720.1980.1900.0410.0870.3470.0330.045-0.022-0.066-0.0600.0160.0011.0000.0920.3170.6210.2690.2800.1740.6160.1800.130-0.100-0.033-0.033-0.0270.0190.0160.2910.0490.0260.286-0.0940.0330.0090.0000.1000.0860.0890.0010.0070.0350.4200.3560.3230.039-0.0010.0030.2800.2480.2860.0040.002-0.045-0.051-0.3750.3750.226-0.3750.0540.1350.100-0.231-0.0320.2700.0000.3050.283-0.0290.0060.089
Time_With_Curr_Empr0.4280.0890.2010.0560.043-0.049-0.083-0.098-0.088-0.056-0.0370.1850.0420.0520.1010.0210.0120.0990.2510.2660.0720.0380.035-0.050-0.094-0.0970.0120.0070.0921.0000.0020.0920.0070.005-0.0130.060-0.065-0.063-0.004-0.127-0.110-0.1250.0540.0470.031-0.034-0.0340.031-0.0240.0160.0020.0000.0070.0080.0150.0010.0100.0000.0880.0770.0730.0230.0090.0150.0310.0320.033-0.097-0.099-0.095-0.099-0.0890.089-0.028-0.089-0.065-0.012-0.020-0.129-0.0880.0290.0240.0360.0260.0700.050-0.056
Tot_Active_TL0.041-0.6140.1940.2010.3270.3860.4000.3530.3220.3770.4740.0470.0380.0290.1280.1970.1760.1540.0450.1770.4320.3120.3600.4250.3620.3190.3740.0010.3170.0021.0000.2740.5290.2980.2700.7420.7300.5940.6650.4400.2890.3730.0450.0630.2090.3930.4410.2080.6590.008-0.0010.0000.1980.1630.176-0.0020.0010.0000.2700.2880.2900.0190.0030.0040.1480.1160.2220.2610.2580.2470.2370.286-0.2860.4150.2860.4210.0940.1630.2820.4140.1950.1160.2100.188-0.018-0.3410.533
Tot_Closed_TL0.223-0.1230.6440.1870.0620.1540.1490.1020.0870.1320.2670.2570.0150.0220.0260.5490.2010.0870.0500.1280.3650.0740.2810.2130.1050.0810.198-0.0010.6210.0920.2741.0000.1940.6550.5140.7920.2450.1820.3690.0890.0670.0830.0150.0150.3030.0760.0320.2960.1610.028-0.0020.0000.1150.1140.085-0.0060.0060.0310.3330.2350.1950.0360.0020.0060.2780.2450.3010.0920.0890.0640.049-0.7740.7740.008-0.7740.0530.5240.445-0.301-0.0450.2810.0230.3200.299-0.0070.1000.282
Tot_Missed_Pmnt0.013-0.5210.0650.0730.0490.1140.1190.1200.1200.1190.1970.0090.0090.0210.0370.2340.1460.0750.0220.0780.3350.0620.1640.1630.1420.1390.1700.0010.2690.0070.5290.1941.0000.2150.1900.4240.4970.5100.2820.2180.1940.2250.0150.0090.0770.1400.1710.0750.2620.006-0.0040.0000.0450.0390.048-0.014-0.0030.0000.2060.2150.218-0.001-0.004-0.0000.0750.0760.0780.1360.1350.1350.1330.101-0.1010.4070.1010.4040.0930.1240.2290.4020.0660.0260.0770.066-0.102-0.1990.217
Tot_TL_closed_L12M0.054-0.3060.2120.0930.1600.1470.1890.1770.1610.1380.3900.0690.0200.0100.0250.2500.1390.0320.0250.0750.2800.1980.2590.2630.2090.1750.2050.0020.2800.0050.2980.6550.2151.0000.8010.5540.4270.3100.4160.2150.1490.1810.0400.0420.1740.2380.1860.1690.1950.007-0.0020.0000.1350.1360.087-0.012-0.0050.0000.2210.2130.1670.0110.0100.0090.1330.1110.1790.1340.1320.1350.126-0.4500.4500.082-0.4500.1560.9330.7630.0200.1260.1590.0930.1720.162-0.020-0.0740.315
Tot_TL_closed_L6M0.021-0.3160.1240.0680.1740.1440.1900.1830.1700.1390.3900.0180.0280.0140.0340.1520.1180.0100.0230.0710.2330.2290.2350.2630.2230.1970.189-0.0030.174-0.0130.2700.5140.1900.8011.0000.4470.4170.3420.3940.2310.1640.1980.0530.0580.1030.1960.2100.1010.1910.0040.0010.0000.0990.0950.066-0.0080.0010.0000.1400.1450.140-0.0030.0030.0110.0650.0440.1080.1410.1400.1570.150-0.3360.3360.049-0.3360.1930.7340.9730.0900.1780.0950.0960.1010.097-0.020-0.1110.300
Total_TL0.169-0.4390.5310.2190.1200.3070.3130.2590.2330.2880.4380.1840.0170.0210.0580.4970.2200.1370.0550.1830.4900.1270.3670.3700.2700.2330.3220.0030.6160.0600.7420.7920.4240.5540.4471.0000.5740.4560.6200.3090.2090.2670.0200.0210.3240.2770.2730.3190.4740.022-0.0010.0000.1860.1660.152-0.0050.0050.0240.3700.3100.2810.0370.0070.0090.2750.2350.3280.2030.1990.1840.171-0.3220.3220.244-0.3220.2830.3570.347-0.0270.2220.3030.0450.3340.311-0.020-0.1090.484
Total_TL_opened_L12M-0.064-0.825-0.0890.0730.2290.2570.3190.3450.3110.2620.515-0.0970.0250.0180.0470.1690.1400.0320.0090.1110.3080.2670.2690.3940.4070.3530.282-0.0010.180-0.0650.7300.2450.4970.4270.4170.5741.0000.7540.5680.5510.3380.4440.0410.0650.0420.3060.3890.0420.493-0.012-0.0130.0000.0750.0340.086-0.027-0.0180.0000.1060.1510.166-0.032-0.025-0.0120.0190.0130.0450.2690.2700.3020.3010.162-0.1620.4170.1620.5760.2820.3440.7030.6210.0340.1350.0360.032-0.112-0.3450.467
Total_TL_opened_L6M-0.076-0.846-0.0740.0420.2000.2090.2650.2890.2940.2140.413-0.1520.0330.0270.0430.1280.1130.0120.0060.0920.2610.2530.2430.3390.3530.3580.2520.0030.130-0.0630.5940.1820.5100.3100.3420.4560.7541.0000.4620.4640.3440.4890.0340.0630.0060.1850.2690.0060.404-0.003-0.0070.000-0.001-0.0290.028-0.024-0.0160.0000.0670.0950.117-0.016-0.012-0.0020.0040.0050.0070.2880.2890.3380.3380.141-0.1410.3770.1410.9120.1860.2720.4930.9200.0010.1320.0090.005-0.137-0.2860.402
Unsecured_TL0.065-0.4610.187-0.0200.3470.4260.4810.4250.3920.4040.760-0.0340.0460.0310.076-0.0300.0550.0490.0480.1780.3110.3860.4880.5600.4580.4040.4390.004-0.100-0.0040.6650.3690.2820.4160.3940.6200.5680.4621.0000.4810.3370.4130.0710.0830.1530.3520.3720.1520.7960.006-0.0100.0000.1740.1520.136-0.009-0.0070.0000.0930.0840.0820.0180.0080.0050.0900.0630.1660.2920.2890.3060.294-0.0030.0030.103-0.0030.3100.2950.3300.1980.3110.1460.1280.1530.1400.021-0.2120.606
enq_L12m-0.141-0.502-0.1040.1030.2780.2990.6110.6670.6460.3060.456-0.4500.0430.0390.018-0.0430.0140.0110.0710.1420.0720.2350.2640.6810.7430.7090.2660.015-0.033-0.1270.4400.0890.2180.2150.2310.3090.5510.4640.4811.0000.8140.9100.0690.0870.0950.3100.3510.0950.418-0.012-0.0090.0000.1410.1100.119-0.013-0.0020.000-0.080-0.054-0.043-0.0020.002-0.0010.0530.0320.1050.4140.4150.5240.5250.153-0.1530.1880.1530.3670.1430.1940.4320.4070.0900.2770.0900.080-0.164-0.2690.842
enq_L3m-0.109-0.367-0.0620.0800.1630.2010.5310.5820.6120.2070.319-0.5130.0360.0000.041-0.0340.0230.0000.0680.1040.0430.1400.1820.5570.6060.6510.1770.022-0.033-0.1100.2890.0670.1940.1490.1640.2090.3380.3440.3370.8141.0000.8960.0570.0820.0930.2250.2390.0920.280-0.0090.0000.0000.1180.1050.089-0.0050.0020.000-0.062-0.044-0.0360.0080.010-0.0010.0590.0390.1000.3490.3500.4500.4500.091-0.0910.1170.0910.2880.1010.1380.2490.3130.0880.2250.0890.080-0.252-0.1790.698
enq_L6m-0.136-0.468-0.0800.0900.2190.2500.5720.6260.6530.2580.389-0.4900.0380.0310.029-0.0320.0190.0060.0750.1260.0670.1990.2310.6330.6890.7380.2300.017-0.027-0.1250.3730.0830.2250.1810.1980.2670.4440.4890.4130.9100.8961.0000.0590.0780.0880.2560.2880.0870.351-0.014-0.0030.0000.1150.0920.096-0.009-0.0010.000-0.072-0.052-0.0410.0050.0090.0020.0560.0370.0960.4220.4220.5670.5680.120-0.1200.1680.1200.4280.1180.1640.3270.4530.0820.2750.0840.074-0.222-0.2230.776
first_prod_enq20.0660.0560.1010.0720.4550.1300.1230.0960.1040.1950.0710.0890.0740.0770.4220.0170.0500.1940.0820.0300.0230.2830.0880.1070.0660.0710.1061.0000.0190.0540.0450.0150.0150.0400.0530.0200.0410.0340.0710.0690.0570.0591.0000.3050.0350.0110.0160.0260.0190.0170.0030.0110.0520.0410.0530.0150.0140.0140.0620.0620.0520.0110.0000.0080.0380.0280.0600.1060.1110.0970.1030.0770.0770.0030.0770.0600.0830.0740.0790.0650.0200.0780.0650.0470.0590.0480.072
last_prod_enq20.0420.0740.0520.0320.4180.0900.1220.1700.2190.1830.0690.1180.0660.0500.2780.0140.0510.1260.0750.0210.0250.2680.0530.1090.0840.1360.1171.0000.0160.0470.0630.0150.0090.0420.0580.0210.0650.0630.0830.0870.0820.0780.3051.0000.0120.0040.0070.0160.0000.0000.0000.0180.0310.0300.0230.0090.0090.0040.0270.0220.0230.0000.0000.0150.0190.0200.0210.2190.2210.2200.2190.0830.0840.0180.0830.0870.0690.0660.1050.0840.0130.0960.0470.0420.1200.0620.069
max_delinquency_level0.073-0.0090.2970.1670.0190.1110.1250.0840.0760.0980.054-0.2020.0250.0290.0300.2200.1480.1190.0640.0780.1600.0100.0960.1070.0630.0650.0680.0150.2910.0310.2090.3030.0770.1740.1030.3240.0420.0060.1530.0950.0930.0880.0350.0121.0000.5180.3330.9890.1240.031-0.0040.0000.6540.5570.4860.0000.0060.0770.1320.1040.0870.0510.0190.0200.7780.6320.9820.0340.0310.0280.020-0.1650.1650.027-0.165-0.0220.1130.073-0.183-0.0810.9810.0000.9760.958-0.006-0.0070.196
max_deliq_12mts-0.032-0.299-0.0290.1480.0470.2320.3050.2740.2600.2230.309-0.1720.0300.0020.043-0.0250.0360.1050.0120.0850.0990.0200.1420.2700.2490.2370.1350.0000.049-0.0340.3930.0760.1400.2380.1960.2770.3060.1850.3520.3100.2250.2560.0110.0040.5181.0000.8340.5190.297-0.014-0.0160.0000.7700.6460.572-0.027-0.0120.078-0.103-0.088-0.092-0.013-0.0020.0130.3190.2160.5450.1310.1290.1320.1270.175-0.1750.1420.1750.1500.2020.1800.1930.1370.5130.0100.4710.4230.055-0.2370.366
max_deliq_6mts-0.045-0.390-0.0840.1530.0510.2660.3380.3080.2900.2560.350-0.1500.0280.0000.050-0.0560.0210.0960.0290.0970.0830.0300.1500.3020.2860.2680.152-0.0110.026-0.0340.4410.0320.1710.1860.2100.2730.3890.2690.3720.3510.2390.2880.0160.0070.3330.8341.0000.3330.328-0.020-0.0160.0000.5320.3310.639-0.028-0.0110.000-0.150-0.132-0.134-0.032-0.0110.0030.1960.1170.3630.1570.1560.1580.1550.257-0.2570.1840.2570.2340.1290.1930.3040.2310.3250.0000.3020.2420.065-0.3040.392
max_recent_level_of_deliq0.073-0.0080.2930.1730.0250.1040.1230.0850.0780.0930.057-0.1990.0220.0140.0220.2120.0920.1180.0430.0790.1570.0060.0980.1100.0650.0660.0710.0140.2860.0310.2080.2960.0750.1690.1010.3190.0420.0060.1520.0950.0920.0870.0260.0160.9890.5190.3331.0000.1240.033-0.0040.0000.6520.5490.4830.0010.0060.2350.1270.1020.0860.0500.0200.0220.7230.5800.9710.0340.0300.0270.019-0.1610.1610.023-0.161-0.0220.1100.072-0.181-0.0810.9920.0110.9710.959-0.001-0.0100.196
max_unsec_exposure_inPct0.040-0.4270.115-0.0390.0290.4080.4230.3690.3330.4090.496-0.0200.0600.0000.000-0.0230.0230.0360.0140.0750.2540.0350.5050.5010.4140.3460.5470.003-0.094-0.0240.6590.1610.2620.1950.1910.4740.4930.4040.7960.4180.2800.3510.0190.0000.1240.2970.3280.1241.000-0.000-0.0000.0000.1540.1250.134-0.008-0.0050.0000.1480.1670.1720.0210.0130.0080.0780.0540.1360.2530.2510.2710.2590.217-0.2170.2270.2170.2980.0680.1240.2280.3000.1180.0000.1230.1080.012-0.2660.489
num_dbt0.0340.0180.076-0.0150.000-0.011-0.020-0.018-0.020-0.009-0.0270.0380.0030.0000.0000.0200.0000.0080.0000.0140.0670.000-0.012-0.011-0.011-0.010-0.0170.0030.0330.0160.0080.0280.0060.0070.0040.022-0.012-0.0030.006-0.012-0.009-0.0140.0170.0000.031-0.014-0.0200.033-0.0001.0000.4320.6390.004-0.0050.0030.0660.0570.0000.0270.0060.0060.2070.079-0.0020.0330.0460.031-0.013-0.014-0.004-0.005-0.0220.022-0.002-0.022-0.008-0.004-0.002-0.035-0.0150.0280.0000.0270.025-0.0060.026-0.008
num_dbt_12mts0.0090.0190.029-0.0080.000-0.008-0.008-0.005-0.004-0.007-0.0200.0030.0000.0000.0240.0020.0000.0070.0150.0110.0240.000-0.0080.000-0.004-0.001-0.0060.0030.0090.002-0.001-0.002-0.004-0.0020.001-0.001-0.013-0.007-0.010-0.0090.000-0.0030.0030.000-0.004-0.016-0.016-0.004-0.0000.4321.0000.764-0.008-0.006-0.0040.0310.0670.0000.000-0.010-0.0090.0800.026-0.001-0.004-0.004-0.005-0.005-0.005-0.000-0.0000.005-0.005-0.0120.005-0.008-0.0030.001-0.018-0.009-0.0040.020-0.003-0.003-0.0020.022-0.004
num_dbt_6mts0.0000.0750.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0020.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0180.0000.0000.0000.0000.0000.6390.7641.0000.0000.0000.0000.0000.2880.0000.0000.0000.0000.1300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.000
num_deliq_12mts0.029-0.0360.1210.1160.1490.1580.1670.1270.1090.1500.094-0.0890.0230.0090.1070.0400.0430.1150.0470.0530.1000.0860.1070.1280.0900.0890.0840.0040.1000.0070.1980.1150.0450.1350.0990.1860.075-0.0010.1740.1410.1180.1150.0520.0310.6540.7700.5320.6520.1540.004-0.0080.0001.0000.8480.759-0.002-0.0000.0300.0540.0460.0400.0120.0150.0160.4110.2820.6980.0580.0550.0420.0350.000-0.0000.0370.000-0.0240.1030.085-0.045-0.0500.6430.0240.5990.529-0.017-0.0970.203
num_deliq_6_12mts0.027-0.0010.1270.0990.1220.1330.1420.0990.0860.1250.082-0.0880.0130.0050.1000.0330.0430.1080.0440.0530.0890.0780.0900.1070.0720.0720.0630.0140.0860.0080.1630.1140.0390.1360.0950.1660.034-0.0290.1520.1100.1050.0920.0410.0300.5570.6460.3310.5490.125-0.005-0.0060.0000.8481.0000.436-0.0000.0030.0290.0550.0460.0390.0150.0180.0180.3860.2800.6050.0400.0370.0310.024-0.0200.0200.014-0.020-0.0480.1100.085-0.079-0.0740.5380.0120.5190.463-0.016-0.0610.179
num_deliq_6mts0.036-0.0560.0940.1060.1500.1620.1520.1140.0920.1520.065-0.0300.0290.0260.0890.0280.0390.1100.0390.0490.0810.0730.0980.1150.0790.0760.079-0.0100.0890.0150.1760.0850.0480.0870.0660.1520.0860.0280.1360.1190.0890.0960.0530.0230.4860.5720.6390.4830.1340.003-0.0040.0000.7590.4361.0000.0020.0030.0070.0470.0410.034-0.0020.0100.0020.3280.2180.5330.0580.0560.0420.0360.019-0.0190.0510.0190.0110.0520.051-0.006-0.0090.4700.0150.4420.340-0.013-0.1050.168
num_lss0.0090.0370.051-0.0210.000-0.013-0.006-0.002-0.003-0.012-0.0260.0150.0150.0000.000-0.0110.020-0.0040.010-0.0010.0400.000-0.007-0.006-0.009-0.004-0.008-0.0070.0010.001-0.002-0.006-0.014-0.012-0.008-0.005-0.027-0.024-0.009-0.013-0.005-0.0090.0150.0090.000-0.027-0.0280.001-0.0080.0660.0310.000-0.002-0.0000.0021.0000.4730.7160.007-0.002-0.0030.0990.0560.0250.0080.017-0.0030.0000.0010.001-0.0000.008-0.008-0.0120.008-0.021-0.010-0.007-0.032-0.0230.0010.013-0.004-0.004-0.0040.020-0.008
num_lss_12mts0.0030.0250.024-0.0070.000-0.010-0.006-0.003-0.000-0.010-0.007-0.0010.0000.0000.0000.0040.013-0.0050.0000.0070.0120.000-0.0070.0110.0040.009-0.009-0.0160.0070.0100.0010.006-0.003-0.0050.0010.005-0.018-0.016-0.007-0.0020.002-0.0010.0140.0090.006-0.012-0.0110.006-0.0050.0570.0670.288-0.0000.0030.0030.4731.0000.816-0.007-0.005-0.0070.0290.0300.0550.0110.0160.006-0.008-0.0080.0090.009-0.0040.004-0.012-0.004-0.014-0.0030.003-0.016-0.0140.0060.0270.0040.0040.0030.0200.003
num_lss_6mts0.0140.0140.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0440.0160.0000.0000.0000.0000.0070.0000.0000.0000.0000.0001.0000.0350.0000.0000.0310.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0140.0040.0770.0780.0000.2350.0000.0000.0000.0000.0300.0290.0070.7160.8161.0000.0000.0000.0000.0000.0980.2040.1300.1890.0830.0000.0000.0000.0260.0220.0250.0000.0220.0000.0000.0070.0000.0000.2670.0110.0240.0310.0000.0210.000
num_std0.159-0.0700.3590.0660.0410.013-0.037-0.060-0.0640.003-0.1230.4020.0250.0220.1630.3360.2490.1470.0960.0580.3890.1210.1620.008-0.065-0.0710.108-0.0100.4200.0880.2700.3330.2060.2210.1400.3700.1060.0670.093-0.080-0.062-0.0720.0620.0270.132-0.103-0.1500.1270.1480.0270.0000.0000.0540.0550.0470.007-0.0070.0001.0000.8810.8210.1230.0720.0440.1450.1380.130-0.021-0.023-0.048-0.056-0.1480.1480.262-0.1480.0320.1310.095-0.163-0.0310.1170.0000.1340.117-0.028-0.0220.002
num_std_12mts0.122-0.1240.2420.0520.0340.009-0.027-0.045-0.0490.003-0.1240.3830.0270.0240.1550.2860.2640.1470.0840.0540.3370.1020.1590.024-0.043-0.0480.116-0.0070.3560.0770.2880.2350.2150.2130.1450.3100.1510.0950.084-0.054-0.044-0.0520.0620.0220.104-0.088-0.1320.1020.1670.006-0.0100.0000.0460.0460.041-0.002-0.0050.0000.8811.0000.9480.0810.0530.0350.1170.1090.103-0.020-0.021-0.034-0.040-0.0440.0440.308-0.0440.0630.1380.109-0.0670.0170.0930.0100.1040.091-0.025-0.0740.008
num_std_6mts0.107-0.1450.2030.0470.0410.011-0.020-0.037-0.0410.005-0.1190.3640.0290.0260.1480.2630.2420.1420.0740.0520.3020.1080.1560.027-0.032-0.0370.117-0.0060.3230.0730.2900.1950.2180.1670.1400.2810.1660.1170.082-0.043-0.036-0.0410.0520.0230.087-0.092-0.1340.0860.1720.006-0.0090.0000.0400.0390.034-0.003-0.0070.0000.8210.9481.0000.0630.0410.0320.0990.0910.087-0.015-0.016-0.025-0.031-0.0060.0060.312-0.0060.0880.0930.107-0.0290.0470.0770.0030.0870.075-0.027-0.0950.012
num_sub0.0270.0340.101-0.0040.012-0.030-0.010-0.006-0.001-0.030-0.0320.0490.0100.0000.0000.0180.0140.0000.0060.0070.0980.011-0.010-0.008-0.010-0.002-0.0190.0000.0390.0230.0190.036-0.0010.011-0.0030.037-0.032-0.0160.018-0.0020.0080.0050.0110.0000.051-0.013-0.0320.0500.0210.2070.0800.1300.0120.015-0.0020.0990.0290.0000.1230.0810.0631.0000.5020.2790.0640.0780.045-0.003-0.003-0.002-0.002-0.0260.0260.015-0.026-0.0130.006-0.007-0.063-0.0270.0470.0000.0440.040-0.0130.0380.009
num_sub_12mts0.0060.0210.037-0.0150.000-0.018-0.008-0.0040.002-0.018-0.0140.0130.0000.0000.000-0.0110.015-0.0040.0000.0090.0550.000-0.018-0.014-0.011-0.003-0.0200.000-0.0010.0090.0030.002-0.0040.0100.0030.007-0.025-0.0120.0080.0020.0100.0090.0000.0000.019-0.002-0.0110.0200.0130.0790.0260.0000.0150.0180.0100.0560.0300.0980.0720.0530.0410.5021.0000.5550.0240.0200.019-0.001-0.000-0.005-0.0060.001-0.0010.0020.001-0.0070.0120.004-0.030-0.0130.0210.0000.0170.013-0.0060.0150.006
num_sub_6mts0.0120.0030.014-0.0050.000-0.012-0.006-0.003-0.000-0.011-0.0090.0060.0000.0000.000-0.0090.000-0.0020.0050.0130.0380.011-0.006-0.004-0.0030.002-0.013-0.0010.0030.0150.0040.006-0.0000.0090.0110.009-0.012-0.0020.005-0.001-0.0010.0020.0080.0150.0200.0130.0030.0220.008-0.002-0.0010.0000.0160.0180.0020.0250.0550.2040.0440.0350.0320.2790.5551.0000.0300.0150.020-0.007-0.007-0.002-0.004-0.0020.0020.002-0.0020.0040.0100.012-0.012-0.0010.0230.0000.0160.0150.005-0.0040.005
num_times_30p_dpd0.0720.0010.2720.1140.0410.0660.0690.0340.0300.0510.001-0.1240.0050.0300.0550.2370.1030.0990.0510.0650.1520.0210.0590.0600.0320.0350.0310.0090.2800.0310.1480.2780.0750.1330.0650.2750.0190.0040.0900.0530.0590.0560.0380.0190.7780.3190.1960.7230.0780.033-0.0040.0000.4110.3860.3280.0080.0110.1300.1450.1170.0990.0640.0240.0301.0000.7960.7330.0130.0100.0220.016-0.1720.1720.033-0.172-0.0140.0740.038-0.164-0.0670.6970.0080.7080.641-0.0220.0300.127
num_times_60p_dpd0.0680.0020.2440.0710.0420.0350.0330.0080.0060.017-0.015-0.0830.0090.0280.0430.2260.1040.0710.0450.0600.1450.0270.0360.0370.0150.0160.0110.0120.2480.0320.1160.2450.0760.1110.0440.2350.0130.0050.0630.0320.0390.0370.0280.0200.6320.2160.1170.5800.0540.046-0.0040.0000.2820.2800.2180.0170.0160.1890.1380.1090.0910.0780.0200.0150.7961.0000.5800.0040.0020.0150.011-0.1580.1580.031-0.158-0.0070.0590.021-0.141-0.0510.5500.0110.5600.495-0.0310.0400.089
num_times_delinquent0.075-0.0110.2990.1820.0960.1320.1450.0970.0860.1180.063-0.1870.0120.0370.1050.2060.1250.1240.0710.0840.1590.0880.1090.1200.0720.0720.0790.0120.2860.0330.2220.3010.0780.1790.1080.3280.0450.0070.1660.1050.1000.0960.0600.0210.9820.5450.3630.9710.1360.031-0.0050.0000.6980.6050.533-0.0030.0060.0830.1300.1030.0870.0450.0190.0200.7330.5801.0000.0400.0360.0300.021-0.1570.1570.028-0.157-0.0220.1170.077-0.183-0.0820.9620.0000.9780.945-0.002-0.0210.214
pct_CC_enq_L6m_of_L12m-0.091-0.2540.0120.0490.4780.4630.6010.6850.7590.4690.174-0.1920.0620.0490.032-0.0040.0280.0250.0890.1280.0380.2110.2090.3250.3160.3040.1990.0020.004-0.0970.2610.0920.1360.1340.1410.2030.2690.2880.2920.4140.3490.4220.1060.2190.0340.1310.1570.0340.253-0.013-0.0050.0000.0580.0400.0580.000-0.0080.000-0.021-0.020-0.015-0.003-0.001-0.0070.0130.0040.0401.0000.9990.2610.2570.052-0.0520.1050.0520.2240.0740.1040.1450.2340.0310.2280.0370.030-0.109-0.1730.400
pct_CC_enq_L6m_of_ever-0.093-0.2550.0070.0470.4800.4580.5990.6860.7590.4660.174-0.1950.0610.0500.044-0.0050.0330.0240.0800.1270.0370.2230.2050.3240.3150.3040.1960.0020.002-0.0990.2580.0890.1350.1320.1400.1990.2700.2890.2890.4150.3500.4220.1110.2210.0310.1290.1560.0300.251-0.014-0.0050.0000.0550.0370.0560.001-0.0080.000-0.023-0.021-0.016-0.003-0.000-0.0070.0100.0020.0360.9991.0000.2610.2580.053-0.0530.1050.0530.2240.0730.1040.1490.2370.0280.2220.0330.026-0.109-0.1720.398
pct_PL_enq_L6m_of_L12m-0.103-0.302-0.0540.0160.2040.1480.2930.3100.3080.1590.267-0.3850.0460.0420.023-0.0210.036-0.0050.0720.0770.0390.3270.2650.6430.7430.8580.2710.016-0.045-0.0950.2470.0640.1350.1350.1570.1840.3020.3380.3060.5240.4500.5670.0970.2200.0280.1320.1580.0270.271-0.004-0.0000.0000.0420.0310.0420.0010.0090.000-0.048-0.034-0.025-0.002-0.005-0.0020.0220.0150.0300.2610.2611.0000.9940.068-0.0680.0920.0680.2860.0970.1350.2120.3070.0250.2120.0280.021-0.150-0.1240.456
pct_PL_enq_L6m_of_ever-0.112-0.302-0.0700.0110.2440.1350.2850.3060.3050.1480.261-0.3930.0490.0400.042-0.0260.059-0.0110.0920.0720.0350.3730.2470.6350.7450.8580.2540.017-0.051-0.0990.2370.0490.1330.1260.1500.1710.3010.3380.2940.5250.4500.5680.1030.2190.0200.1270.1550.0190.259-0.005-0.0000.0000.0350.0240.036-0.0000.0090.026-0.056-0.040-0.031-0.002-0.006-0.0040.0160.0110.0210.2570.2580.9941.0000.076-0.0760.0920.0760.2880.0930.1300.2230.3120.0170.2060.0200.013-0.150-0.1210.448
pct_active_tl-0.191-0.248-0.532-0.0700.2660.0660.0780.0940.0890.081-0.013-0.2080.0410.0360.114-0.3710.4460.0090.150-0.021-0.1090.307-0.0620.0350.0980.0910.0190.000-0.375-0.0890.286-0.7740.101-0.450-0.336-0.3220.1620.141-0.0030.1530.0910.1200.0770.083-0.1650.1750.257-0.1610.217-0.0220.0050.0000.000-0.0200.0190.008-0.0040.022-0.148-0.044-0.006-0.0260.001-0.002-0.172-0.158-0.1570.0520.0530.0680.0761.000-1.0000.3161.0000.187-0.478-0.3480.4740.267-0.1530.082-0.180-0.174-0.010-0.3330.018
pct_closed_tl0.1910.2480.5320.0700.257-0.066-0.078-0.094-0.089-0.0810.0130.2080.0390.0360.1140.3710.453-0.0090.1530.0210.1090.3110.062-0.035-0.098-0.091-0.019-0.0000.3750.089-0.2860.774-0.1010.4500.3360.322-0.162-0.1410.003-0.153-0.091-0.1200.0770.0840.165-0.175-0.2570.161-0.2170.022-0.0050.000-0.0000.020-0.019-0.0080.0040.0250.1480.0440.0060.026-0.0010.0020.1720.1580.157-0.052-0.053-0.068-0.076-1.0001.000-0.316-1.000-0.1870.4780.348-0.474-0.2670.1530.0770.1800.1740.0100.333-0.018
pct_currentBal_all_TL-0.052-0.502-0.0980.0230.0160.1320.1000.0900.0740.156-0.0610.0300.0030.0000.0000.1620.0000.1420.0150.0570.2270.0000.1390.0960.0960.0830.174-0.0100.226-0.0280.4150.0080.4070.0820.0490.2440.4170.3770.1030.1880.1170.1680.0030.0180.0270.1420.1840.0230.227-0.002-0.0120.0000.0370.0140.051-0.012-0.0120.0000.2620.3080.3120.0150.0020.0020.0330.0310.0280.1050.1050.0920.0920.316-0.3161.0000.3160.395-0.0080.0050.3730.3870.0160.0000.0200.014-0.123-0.3060.132
pct_of_active_TLs_ever-0.191-0.248-0.532-0.0700.2660.0660.0780.0940.0890.081-0.013-0.2080.0410.0360.114-0.3710.4460.0090.150-0.021-0.1090.307-0.0620.0350.0980.0910.0190.000-0.375-0.0890.286-0.7740.101-0.450-0.336-0.3220.1620.141-0.0030.1530.0910.1200.0770.083-0.1650.1750.257-0.1610.217-0.0220.0050.0000.000-0.0200.0190.008-0.0040.022-0.148-0.044-0.006-0.0260.001-0.002-0.172-0.158-0.1570.0520.0530.0680.0761.000-1.0000.3161.0000.187-0.478-0.3480.4740.267-0.1530.082-0.180-0.174-0.010-0.3330.018
pct_opened_TLs_L6m_of_L12m-0.090-0.815-0.146-0.0110.2330.1330.1860.2080.2270.1370.269-0.1600.0290.0350.0270.0720.1460.0000.0550.0540.1540.2540.1600.2420.2650.2900.1720.0020.054-0.0650.4210.0530.4040.1560.1930.2830.5760.9120.3100.3670.2880.4280.0600.087-0.0220.1500.234-0.0220.298-0.008-0.0080.000-0.024-0.0480.011-0.021-0.0140.0000.0320.0630.088-0.013-0.0070.004-0.014-0.007-0.0220.2240.2240.2860.2880.187-0.1870.3950.1871.0000.0840.1560.4890.958-0.0250.121-0.020-0.024-0.142-0.2660.287
pct_tl_closed_L12M0.014-0.2030.0940.0270.1890.0530.1060.1110.1040.0480.3130.0190.0300.0320.0850.1300.220-0.0090.0670.0280.1530.2510.1670.1750.1500.1280.119-0.0010.135-0.0120.0940.5240.0930.9330.7340.3570.2820.1860.2950.1430.1010.1180.0830.0690.1130.2020.1290.1100.068-0.004-0.0030.0000.1030.1100.052-0.010-0.0030.0000.1310.1380.0930.0060.0120.0100.0740.0590.1170.0740.0730.0970.093-0.4780.478-0.008-0.4780.0841.0000.7640.0170.0680.1060.0660.1090.108-0.008-0.0160.210
pct_tl_closed_L6M0.001-0.2640.0590.0300.1690.0910.1440.1450.1370.0870.346-0.0070.0220.0250.0610.0920.143-0.0100.0480.0470.1650.2210.1820.2150.1900.1710.139-0.0050.100-0.0200.1630.4450.1240.7630.9730.3470.3440.2720.3300.1940.1380.1640.0740.0660.0730.1800.1930.0720.124-0.0020.0010.0000.0850.0850.051-0.0070.0030.0070.0950.1090.107-0.0070.0040.0120.0380.0210.0770.1040.1040.1350.130-0.3480.3480.005-0.3480.1560.7641.0000.0980.1490.0690.0650.0690.069-0.014-0.0850.243
pct_tl_open_L12M-0.205-0.683-0.579-0.1200.2200.0560.1360.2060.1870.0740.261-0.2740.0340.0400.116-0.1440.320-0.0580.171-0.006-0.0300.2580.0280.1890.2750.2390.068-0.004-0.231-0.1290.282-0.3010.2290.0200.090-0.0270.7030.4930.1980.4320.2490.3270.0790.105-0.1830.1930.304-0.1810.228-0.035-0.0180.000-0.045-0.079-0.006-0.032-0.0160.000-0.163-0.067-0.029-0.063-0.030-0.012-0.164-0.141-0.1830.1450.1490.2120.2230.474-0.4740.3730.4740.4890.0170.0981.0000.590-0.1780.091-0.199-0.189-0.129-0.3470.192
pct_tl_open_L6M-0.133-0.815-0.263-0.0460.1980.1200.1850.2230.2400.1300.295-0.2240.0270.0260.0830.0040.206-0.0240.1320.0440.1240.2260.1400.2490.2930.3120.1600.003-0.032-0.0880.414-0.0450.4020.1260.1780.2220.6210.9200.3110.4070.3130.4530.0650.084-0.0810.1370.231-0.0810.300-0.015-0.0090.000-0.050-0.074-0.009-0.023-0.0140.000-0.0310.0170.047-0.027-0.013-0.001-0.067-0.051-0.0820.2340.2370.3070.3120.267-0.2670.3870.2670.9580.0680.1490.5901.000-0.0820.103-0.082-0.082-0.153-0.2880.289
recent_level_of_deliq0.069-0.0020.2810.1670.0280.0940.1150.0810.0750.0850.056-0.2110.0180.0180.0230.1970.0490.1120.0350.0720.1490.0060.0910.1040.0610.0630.0660.0150.2700.0290.1950.2810.0660.1590.0950.3030.0340.0010.1460.0900.0880.0820.0200.0130.9810.5130.3250.9920.1180.028-0.0040.0000.6430.5380.4700.0010.0060.2670.1170.0930.0770.0470.0210.0230.6970.5500.9620.0310.0280.0250.017-0.1530.1530.016-0.153-0.0250.1060.069-0.178-0.0821.0000.0000.9640.9620.002-0.0050.188
response_flag0.0320.0370.0190.0160.1770.1180.2150.2620.2850.2130.0840.1750.0520.0160.0080.0090.0000.0080.0310.0140.0050.1400.0930.2510.2860.2870.1691.0000.0000.0240.1160.0230.0260.0930.0960.0450.1350.1320.1280.2770.2250.2750.0780.0960.0000.0100.0000.0110.0000.0000.0200.0000.0240.0120.0150.0130.0270.0110.0000.0100.0030.0000.0000.0000.0080.0110.0000.2280.2220.2120.2060.0820.0770.0000.0820.1210.0660.0650.0910.1030.0001.0000.0300.0340.0450.0450.235
time_since_first_deliquency0.078-0.0040.3150.1840.1170.1200.1320.0860.0790.1080.054-0.2030.0170.0400.1170.2300.2360.1150.0880.0850.1530.0980.1030.1110.0650.0660.0760.0140.3050.0360.2100.3200.0770.1720.1010.3340.0360.0090.1530.0900.0890.0840.0650.0470.9760.4710.3020.9710.1230.027-0.0030.0000.5990.5190.442-0.0040.0040.0240.1340.1040.0870.0440.0170.0160.7080.5600.9780.0370.0330.0280.020-0.1800.1800.020-0.180-0.0200.1090.069-0.199-0.0820.9640.0301.0000.9760.004-0.0030.201
time_since_recent_deliquency0.064-0.0010.2870.1640.0870.0950.1110.0760.0700.0860.054-0.2300.0140.0240.1060.2180.2100.0970.0540.0710.1390.0730.0860.0960.0560.0560.0630.0180.2830.0260.1880.2990.0660.1620.0970.3110.0320.0050.1400.0800.0800.0740.0470.0420.9580.4230.2420.9590.1080.025-0.0030.0000.5290.4630.340-0.0040.0040.0310.1170.0910.0750.0400.0130.0150.6410.4950.9450.0300.0260.0210.013-0.1740.1740.014-0.174-0.0240.1080.069-0.189-0.0820.9620.0340.9761.0000.0050.0050.178
time_since_recent_enq0.0620.1410.0750.1360.0880.0130.2440.2480.2800.0020.0280.2980.0180.0170.010-0.1040.0430.0290.0650.034-0.0610.0800.0050.1560.1360.144-0.003-0.011-0.0290.070-0.018-0.007-0.102-0.020-0.020-0.020-0.112-0.1370.021-0.164-0.252-0.2220.0590.120-0.0060.0550.065-0.0010.012-0.006-0.0020.000-0.017-0.016-0.013-0.0040.0030.000-0.028-0.025-0.027-0.013-0.0060.005-0.022-0.031-0.002-0.109-0.109-0.150-0.150-0.0100.010-0.123-0.010-0.142-0.008-0.014-0.129-0.1530.0020.0450.0040.0051.0000.093-0.018
time_since_recent_payment0.0990.3970.209-0.0830.134-0.267-0.230-0.201-0.177-0.269-0.1380.1030.0220.0000.0410.0460.072-0.0480.063-0.067-0.0560.103-0.139-0.170-0.159-0.150-0.158-0.0020.0060.050-0.3410.100-0.199-0.074-0.111-0.109-0.345-0.286-0.212-0.269-0.179-0.2230.0480.062-0.007-0.237-0.304-0.010-0.2660.0260.0220.094-0.097-0.061-0.1050.0200.0200.021-0.022-0.074-0.0950.0380.015-0.0040.0300.040-0.021-0.173-0.172-0.124-0.121-0.3330.333-0.306-0.333-0.266-0.016-0.085-0.347-0.288-0.0050.045-0.0030.0050.0931.000-0.238
tot_enq-0.028-0.4090.1380.2460.3490.4010.6830.6570.6360.3920.521-0.2740.0470.0370.087-0.0180.0400.0950.0010.2180.1330.2970.3860.7460.6840.6560.3520.0110.089-0.0560.5330.2820.2170.3150.3000.4840.4670.4020.6060.8420.6980.7760.0720.0690.1960.3660.3920.1960.489-0.008-0.0040.0000.2030.1790.168-0.0080.0030.0000.0020.0080.0120.0090.0060.0050.1270.0890.2140.4000.3980.4560.4480.018-0.0180.1320.0180.2870.2100.2430.1920.2890.1880.2350.2010.178-0.018-0.2381.000

Test

AGEAge_Newest_TLAge_Oldest_TLAuto_TLCC_FlagCC_TLCC_enqCC_enq_L12mCC_enq_L6mCC_utilizationConsumer_TLCredit_ScoreEDUCATIONGENDERGL_FlagGold_TLHL_FlagHome_TLMARITALSTATUSNETMONTHLYINCOMEOther_TLPL_FlagPL_TLPL_enqPL_enq_L12mPL_enq_L6mPL_utilizationPROSPECTIDSecured_TLTime_With_Curr_EmprTot_Active_TLTot_Closed_TLTot_Missed_PmntTot_TL_closed_L12MTot_TL_closed_L6MTotal_TLTotal_TL_opened_L12MTotal_TL_opened_L6MUnsecured_TLenq_L12menq_L3menq_L6mfirst_prod_enq2last_prod_enq2max_delinquency_levelmax_deliq_12mtsmax_deliq_6mtsmax_recent_level_of_deliqmax_unsec_exposure_inPctnum_dbtnum_dbt_12mtsnum_dbt_6mtsnum_deliq_12mtsnum_deliq_6_12mtsnum_deliq_6mtsnum_lssnum_lss_12mtsnum_lss_6mtsnum_stdnum_std_12mtsnum_std_6mtsnum_subnum_sub_12mtsnum_sub_6mtsnum_times_30p_dpdnum_times_60p_dpdnum_times_delinquentpct_CC_enq_L6m_of_L12mpct_CC_enq_L6m_of_everpct_PL_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_active_tlpct_closed_tlpct_currentBal_all_TLpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_tl_closed_L12Mpct_tl_closed_L6Mpct_tl_open_L12Mpct_tl_open_L6Mrecent_level_of_deliqresponse_flagtime_since_first_deliquencytime_since_recent_deliquencytime_since_recent_enqtime_since_recent_paymenttot_enq
AGE1.0000.1090.3560.0320.059-0.015-0.058-0.077-0.066-0.034-0.0140.2750.0610.1020.1510.0870.1020.1470.6130.1530.1500.1020.099-0.027-0.095-0.0980.0640.0020.1640.4430.0420.2170.0280.0390.0120.168-0.064-0.0720.059-0.144-0.113-0.1360.0750.0500.074-0.028-0.0480.0750.0420.0240.0030.0000.0320.0340.031-0.0070.0000.0000.1720.1310.1180.0300.0120.0000.0830.0770.074-0.075-0.078-0.106-0.116-0.1860.186-0.036-0.186-0.088-0.003-0.009-0.207-0.1300.0710.0290.0750.0620.0790.105-0.030
Age_Newest_TL0.1091.0000.212-0.0270.103-0.187-0.260-0.288-0.274-0.197-0.4210.1290.0210.0000.065-0.1250.0970.0030.073-0.079-0.2180.114-0.207-0.324-0.341-0.317-0.230-0.016-0.1110.069-0.616-0.134-0.519-0.321-0.329-0.444-0.830-0.846-0.456-0.487-0.342-0.4470.0570.0780.000-0.287-0.374-0.001-0.4150.0330.0200.101-0.0350.003-0.0570.0360.0670.067-0.079-0.142-0.1560.0370.0230.0170.0050.004-0.001-0.233-0.234-0.275-0.276-0.2270.227-0.496-0.227-0.812-0.218-0.278-0.683-0.8130.0050.0220.0060.0110.1420.391-0.391
Age_Oldest_TL0.3560.2121.0000.3100.1490.1430.0780.003-0.0020.113-0.0100.4790.0340.0560.2520.3260.2850.1720.2360.1450.3270.2050.2090.062-0.051-0.0570.1380.0060.5330.1950.1920.6410.0650.2090.1270.526-0.089-0.0640.183-0.113-0.063-0.0920.1060.0510.298-0.024-0.0800.2930.1200.0700.0350.0300.1270.1330.0990.0440.0000.0000.3730.2520.2140.1060.0380.0000.2820.2490.2980.0180.013-0.062-0.078-0.5320.532-0.074-0.532-0.1390.0930.064-0.577-0.2520.2830.0070.3130.2850.0700.2120.135
Auto_TL0.032-0.0270.3101.0000.1100.1070.1640.1500.1540.105-0.0190.1220.0040.0320.057-0.0150.0620.0220.0570.086-0.0400.1000.0670.0980.0760.0890.0480.0170.4410.0510.2070.1760.0790.0800.0560.2150.0670.048-0.0200.0950.0740.0840.0670.0230.1820.1650.1790.188-0.040-0.005-0.0110.0000.1250.1050.123-0.0030.0000.0000.0580.0510.0460.001-0.0030.0000.1290.0890.1950.0740.0720.0070.002-0.0580.0580.033-0.058-0.0040.0140.019-0.118-0.0360.1800.0360.1950.1730.135-0.0950.245
CC_Flag0.0590.1030.1490.1101.0000.2940.5100.5790.4660.9490.2000.1000.2080.0720.0790.0000.0000.0880.0120.0220.0140.2250.1520.2650.1530.0930.2221.0000.0000.0370.3250.0200.0320.1580.1540.0710.1970.1720.3420.2500.1510.1990.4710.4420.0480.0700.0600.0600.0130.0000.0000.0000.1500.1140.1530.0000.0000.0000.0220.0200.0000.0090.0000.0000.0610.0550.0920.4710.4750.1820.2120.2730.2700.0210.2730.2090.1890.1650.1920.1950.0550.1690.1070.0740.0810.1190.396
CC_TL-0.015-0.1870.1430.1070.2941.0000.6730.5120.4140.9420.126-0.0040.0640.0220.1010.0010.0220.0880.0190.1720.0220.1550.2450.2910.2190.1860.2190.0070.017-0.0500.3730.1400.0840.1310.1290.2840.2320.1920.4110.2770.1950.2350.1090.0690.0990.2220.2540.0950.399-0.0070.0040.0000.1470.1300.147-0.0150.0000.000-0.005-0.005-0.009-0.031-0.0080.0000.0590.0420.1180.4560.4500.1210.1110.070-0.0700.1300.0700.1250.0440.0800.0480.1150.0860.0860.1060.0830.006-0.2640.384
CC_enq-0.058-0.2600.0780.1640.5100.6731.0000.8970.8310.6510.291-0.1480.0800.0540.083-0.0400.0430.0830.0290.1940.0090.2610.3040.5950.5580.5430.2710.003-0.003-0.0750.3910.1450.1010.1860.1770.3060.2960.2440.4660.5940.5220.5560.1390.1450.1290.3030.3340.1250.409-0.012-0.0000.0000.1610.1450.141-0.0010.0000.000-0.048-0.034-0.035-0.0070.0000.0000.0750.0490.1490.6080.6060.2660.2580.074-0.0740.1030.0740.1720.1120.1370.1240.1750.1170.1960.1350.1160.233-0.2330.674
CC_enq_L12m-0.077-0.2880.0030.1500.5790.5120.8971.0000.9210.5120.302-0.2050.0770.0560.048-0.0590.0000.0530.0660.1570.0010.2350.2580.5910.5890.5800.241-0.003-0.025-0.0760.3470.0930.1050.1740.1770.2520.3270.2680.4120.6570.5740.6150.1390.2320.0870.2690.3010.0870.353-0.0090.0030.0000.1230.1010.1040.0040.0000.000-0.064-0.046-0.046-0.0030.0040.0000.0320.0160.1040.6940.6940.2820.2780.095-0.0950.0940.0950.1930.1150.1440.1990.2140.0810.2420.0910.0810.234-0.2040.647
CC_enq_L6m-0.066-0.274-0.0020.1540.4660.4140.8310.9211.0000.4170.299-0.2180.0610.0420.056-0.0630.0130.0520.0550.144-0.0070.2200.2440.5940.6010.6040.223-0.004-0.026-0.0680.3170.0780.1030.1570.1580.2280.2920.2700.3810.6400.6030.6430.1060.2210.0870.2580.2850.0880.320-0.0090.0050.0000.1080.0920.0850.0080.0000.000-0.065-0.045-0.0460.0030.0050.0000.0360.0170.1020.7670.7670.2860.2840.090-0.0900.0790.0900.2090.1080.1320.1790.2280.0830.2640.0920.0830.263-0.1810.631
CC_utilization-0.034-0.1970.1130.1050.9490.9420.6510.5120.4171.0000.130-0.0260.0870.0760.071-0.0000.0050.0640.0320.1570.0220.2330.2340.2890.2240.1900.2110.0080.012-0.0620.3640.1220.0900.1300.1320.2680.2390.1980.3900.2900.2040.2470.2010.1880.0900.2170.2480.0880.397-0.0060.0040.0000.1420.1260.142-0.0130.0000.000-0.013-0.009-0.015-0.029-0.0070.0000.0460.0280.1090.4620.4580.1280.1190.083-0.0830.1500.0830.1320.0470.0840.0670.1260.0790.1930.0980.077-0.007-0.2610.377
Consumer_TL-0.014-0.421-0.010-0.0190.2000.1260.2910.3020.2990.1301.000-0.1490.0290.0230.005-0.0800.029-0.0260.0260.0810.0910.2340.1900.4430.4040.3520.204-0.007-0.155-0.0530.4650.2620.1930.4010.4030.4310.5060.4020.7550.4530.3090.3830.0740.0700.0560.3120.3480.0590.492-0.021-0.0030.0000.1010.0890.069-0.0180.0000.000-0.141-0.134-0.125-0.047-0.0260.000-0.007-0.0170.0640.1650.1640.2560.253-0.0190.019-0.057-0.0190.2710.3300.3630.2700.2980.0580.0810.0540.0580.021-0.1380.520
Credit_Score0.2750.1290.4790.1220.100-0.004-0.148-0.205-0.218-0.026-0.1491.0000.0260.0310.1990.1710.1960.1210.1800.0320.1990.1480.045-0.235-0.338-0.383-0.002-0.0030.2840.1900.0630.2620.0270.0700.0290.196-0.071-0.113-0.021-0.449-0.514-0.4940.0850.110-0.201-0.163-0.143-0.1980.0060.0240.0050.000-0.082-0.081-0.0290.0040.0000.0000.4020.3880.3720.0540.0180.008-0.116-0.081-0.188-0.172-0.175-0.385-0.394-0.2070.2070.045-0.207-0.1260.0180.005-0.255-0.190-0.2070.136-0.204-0.2300.2930.105-0.270
EDUCATION0.0610.0210.0340.0040.2080.0640.0800.0770.0610.0870.0290.0261.0000.0580.0980.0030.0700.0420.1700.0000.0110.1320.0430.0560.0320.0280.0471.0000.0000.0400.0460.0080.0000.0320.0270.0150.0300.0270.0530.0410.0300.0300.0680.0670.0380.0500.0290.0510.0000.0470.0540.0600.0220.0160.0120.0000.0000.0000.0180.0240.0170.0110.0000.0000.0000.0000.0000.0600.0600.0470.0450.0450.0460.0080.0450.0300.0290.0230.0360.0320.0190.0360.0200.0080.0090.0230.069
GENDER0.1020.0000.0560.0320.0720.0220.0540.0560.0420.0760.0230.0310.0581.0000.0140.0000.0000.0000.1110.0220.0090.0390.0120.0360.0310.0210.0311.0000.0000.0460.0360.0000.0000.0160.0290.0000.0230.0100.0370.0340.0090.0250.0680.0590.0200.0080.0210.0000.0230.0000.0230.0190.0140.0000.0050.0090.0210.0210.0000.0070.0250.0000.0000.0000.0310.0280.0320.0390.0420.0410.0380.0290.0340.0360.0290.0290.0390.0440.0430.0210.0110.0000.0470.0240.0360.0140.061
GL_Flag0.1510.0650.2520.0570.0790.1010.0830.0480.0560.0710.0050.1990.0980.0141.0000.0330.0241.0000.0820.0000.0280.0830.0650.0490.0040.0210.0271.0000.0560.1100.1200.0240.0270.0320.0070.0550.0400.0000.0600.0000.0190.0000.4330.2750.0720.0580.0600.0600.0000.0000.0150.0000.1160.1120.1050.0000.0000.0000.1260.1370.1370.0340.0000.0000.0660.0600.1190.0470.0690.0000.0390.1110.1170.0200.1110.0370.0810.0530.1060.0890.0360.0040.1230.1070.0020.0460.107
Gold_TL0.087-0.1250.326-0.0150.0000.001-0.040-0.059-0.063-0.000-0.0800.1710.0030.0000.0331.0000.1680.0240.0300.0450.1510.0330.031-0.024-0.050-0.0560.0220.0030.6720.0370.2070.5580.2280.2610.1630.5070.1760.140-0.013-0.027-0.026-0.0240.0000.0000.225-0.020-0.0560.216-0.0090.008-0.0060.0000.0470.0380.029-0.0100.0000.0000.3620.3120.2830.048-0.0010.0000.2420.2260.2070.0060.004-0.010-0.015-0.3820.3820.159-0.3820.0780.1380.101-0.1490.0100.2040.0260.2310.222-0.0990.064-0.003
HL_Flag0.1020.0970.2850.0620.0000.0220.0430.0000.0130.0050.0290.1960.0700.0000.0240.1681.0000.0190.0780.0000.1570.0210.0300.0290.0000.0280.0231.0000.2080.0360.2070.2030.1500.1910.1280.2200.1460.1230.0560.0000.0310.0160.0390.0530.1530.0350.0320.0820.0030.0000.0000.0000.0280.0320.0460.0000.0060.0060.2650.2920.2710.0330.0000.0000.1300.0950.1210.0230.0520.0000.0440.4520.4610.0160.4520.1610.2200.1690.3290.2300.0440.0120.2360.2170.0530.0370.035
Home_TL0.1470.0030.1720.0220.0880.0880.0830.0530.0520.064-0.0260.1210.0420.0001.0000.0240.0191.0000.0800.1200.0280.0990.0880.051-0.0010.0050.0300.0010.1870.0970.1410.0860.0620.0340.0050.1310.0260.0020.0470.012-0.0040.0040.2010.1270.1270.1090.0930.1290.0210.0080.0060.0000.1270.1190.1160.0180.0000.0000.1380.1370.1310.0210.0080.0000.1140.0880.1320.0500.048-0.013-0.018-0.0060.0060.141-0.006-0.016-0.002-0.011-0.063-0.0350.1240.0000.1210.1020.030-0.0450.100
MARITALSTATUS0.6130.0730.2360.0570.0120.0190.0290.0660.0550.0320.0260.1800.1700.1110.0820.0300.0780.0801.0000.0000.0520.0480.0410.0000.0430.0410.0421.0000.0360.2700.0640.0420.0000.0320.0190.0490.0000.0000.0520.0490.0640.0600.0890.0920.0570.0380.0340.0290.0000.0130.0050.0000.0520.0490.0360.0000.0060.0060.0830.0870.0760.0180.0110.0000.0570.0510.0650.0690.0530.0740.0870.1460.1490.0000.1460.0540.0590.0470.1650.1200.0270.0000.0790.0400.0740.0690.022
NETMONTHLYINCOME0.153-0.0790.1450.0860.0220.1720.1940.1570.1440.1570.0810.0320.0000.0220.0000.0450.0000.1200.0001.0000.0750.0000.1480.1610.1160.0990.1260.0130.1020.2670.1670.1300.0750.0710.0600.1810.1050.0820.1730.1330.1050.1120.0160.0350.0810.0950.1010.0830.0750.0160.0100.0000.0680.0630.062-0.0160.0000.0000.0650.0610.0590.007-0.0060.0000.0690.0660.0870.1150.1130.0550.049-0.0290.0290.071-0.0290.0500.0190.032-0.0120.0370.0760.0000.0850.0690.027-0.0560.215
Other_TL0.150-0.2180.327-0.0400.0140.0220.0090.001-0.0070.0220.0910.1990.0110.0090.0280.1510.1570.0280.0520.0751.0000.0350.0680.0600.0310.0150.0490.0010.3510.0830.4280.3810.3350.2960.2450.4940.3140.2660.3060.0630.0370.0530.0260.0060.1630.0950.0750.1580.2520.0580.0230.0000.1160.0970.0940.0320.0000.0000.4060.3470.3100.0830.0360.0000.1600.1530.1600.0230.0210.0240.022-0.1320.1320.223-0.1320.1550.1700.177-0.0320.1240.1500.0000.1550.136-0.069-0.0460.129
PL_Flag0.1020.1140.2050.1000.2250.1550.2610.2350.2200.2330.2340.1480.1320.0390.0830.0330.0210.0990.0480.0000.0351.0000.3860.4410.2640.2020.8851.0000.0290.0430.3170.0510.0610.2010.2110.0950.2490.2500.3910.2380.1590.2000.2680.2540.0020.0230.0000.0350.0130.0000.0000.0000.0850.0800.0770.0000.0000.0000.1010.1120.0980.0000.0000.0000.0430.0310.0890.2180.2300.3120.3630.3220.3170.0000.3220.2410.2510.2100.2610.2330.0000.1340.0970.0780.0670.0920.368
PL_TL0.099-0.2070.2090.0670.1520.2450.3040.2580.2440.2340.1900.0450.0430.0120.0650.0310.0300.0880.0410.1480.0680.3861.0000.5210.3580.2910.8780.0030.0580.0430.3530.2880.1590.2490.2200.3680.2620.2290.4850.2590.1860.2270.0910.0450.0900.1400.1480.0930.498-0.007-0.0040.0000.1030.0940.0860.0070.0000.0000.1550.1540.148-0.002-0.0120.0000.0590.0410.1040.2140.2110.2390.221-0.0750.0750.131-0.0750.1450.1630.1710.0220.1300.0860.0850.0990.0800.008-0.1350.380
PL_enq-0.027-0.3240.0620.0980.2650.2910.5950.5910.5940.2890.443-0.2350.0560.0360.049-0.0240.0290.0510.0000.1610.0600.4410.5211.0000.8950.8110.481-0.013-0.021-0.0480.4120.2110.1600.2610.2540.3600.3760.3210.5490.6790.5590.6360.0980.1040.0950.2590.2860.0980.484-0.0040.0020.0000.1180.1060.0980.0050.0000.000-0.0000.0160.0170.003-0.0120.0000.0580.0320.1110.3200.3180.6460.6380.021-0.0210.0900.0210.2290.1850.2120.1840.2400.0890.2110.1010.0860.130-0.1720.739
PL_enq_L12m-0.095-0.341-0.0510.0760.1530.2190.5580.5890.6010.2240.404-0.3380.0320.0310.004-0.0500.000-0.0010.0430.1160.0310.2640.3580.8951.0000.8990.363-0.012-0.058-0.0900.3460.1070.1440.2080.2120.2620.3840.3330.4430.7370.6060.6910.0520.0760.0540.2340.2710.0550.394-0.006-0.0010.0000.0770.0650.0650.0050.0000.000-0.069-0.044-0.036-0.008-0.0160.0000.0290.0130.0650.3080.3080.7450.7460.081-0.0810.0880.0810.2500.1570.1850.2650.2810.0490.2230.0560.0480.110-0.1670.675
PL_enq_L6m-0.098-0.317-0.0570.0890.0930.1860.5430.5800.6040.1900.352-0.3830.0280.0210.021-0.0560.0280.0050.0410.0990.0150.2020.2910.8110.8991.0000.286-0.014-0.056-0.0850.3000.0760.1360.1660.1800.2190.3250.3320.3790.6920.6430.7300.0350.0640.0610.2210.2500.0620.321-0.0020.0020.0000.0770.0670.0610.0130.0000.000-0.078-0.053-0.044-0.010-0.0120.0000.0380.0190.0700.2810.2810.8630.8630.080-0.0800.0680.0800.2720.1280.1600.2270.2960.0570.2160.0620.0550.126-0.1550.637
PL_utilization0.064-0.2300.1380.0480.2220.2190.2710.2410.2230.2110.204-0.0020.0470.0310.0270.0220.0230.0300.0420.1260.0490.8850.8780.4810.3630.2861.0000.0080.0220.0190.3620.2070.1740.2050.1840.3210.2770.2370.4350.2670.1820.2270.1060.1080.0590.1320.1440.0610.541-0.008-0.0020.0000.0820.0730.0660.0060.0000.0000.0990.1070.109-0.017-0.0170.0000.0340.0120.0730.1940.1910.2440.2270.002-0.0020.1640.0020.1560.1240.1370.0610.1480.0560.1560.0680.054-0.009-0.1560.351
PROSPECTID0.002-0.0160.0060.0171.0000.0070.003-0.003-0.0040.008-0.007-0.0031.0001.0001.0000.0031.0000.0011.0000.0130.0011.0000.003-0.013-0.012-0.0140.0081.0000.015-0.0000.0070.0040.0180.0030.0020.0090.0130.022-0.0050.0050.0150.0091.0001.000-0.008-0.002-0.004-0.008-0.0010.0100.0011.0000.0020.005-0.0000.0021.0001.0000.0010.0020.0040.003-0.0131.000-0.009-0.010-0.0070.0060.007-0.010-0.0090.002-0.0020.0070.0020.017-0.005-0.0010.0090.018-0.0071.000-0.008-0.009-0.025-0.0170.005
Secured_TL0.164-0.1110.5330.4410.0000.017-0.003-0.025-0.0260.012-0.1550.2840.0000.0000.0560.6720.2080.1870.0360.1020.3510.0290.058-0.021-0.058-0.0560.0220.0151.0000.1110.3210.6220.2680.2830.1780.6200.1840.140-0.094-0.021-0.026-0.0230.0080.0000.2980.0640.0400.294-0.0970.019-0.0050.0000.1210.1070.1060.0030.0000.0000.4350.3700.3350.0470.0100.0000.2910.2500.2900.0160.014-0.044-0.050-0.3870.3870.227-0.3870.0590.1380.104-0.232-0.0240.2800.0180.3050.283-0.0270.0150.098
Time_With_Curr_Empr0.4430.0690.1950.0510.037-0.050-0.075-0.076-0.068-0.062-0.0530.1900.0400.0460.1100.0370.0360.0970.2700.2670.0830.0430.043-0.048-0.090-0.0850.019-0.0000.1111.0000.0180.1010.026-0.004-0.0170.079-0.048-0.044-0.007-0.127-0.106-0.1200.0580.0500.027-0.040-0.0320.029-0.0240.010-0.0050.0000.0040.0010.0250.0180.0000.0000.1110.0960.0910.0190.0120.0700.0390.0390.029-0.081-0.082-0.093-0.097-0.0800.080-0.016-0.080-0.047-0.028-0.030-0.119-0.0720.0260.0000.0300.0190.0800.056-0.056
Tot_Active_TL0.042-0.6160.1920.2070.3250.3730.3910.3470.3170.3640.4650.0630.0460.0360.1200.2070.2070.1410.0640.1670.4280.3170.3530.4120.3460.3000.3620.0070.3210.0181.0000.2840.5300.3200.2870.7400.7320.5980.6620.4320.2830.3640.0450.0660.2020.3950.4410.2030.651-0.0070.0000.0000.2030.1600.185-0.0030.0000.0000.2700.2950.2970.012-0.0070.0000.1440.1120.2130.2600.2570.2240.2160.267-0.2670.4160.2670.4250.1170.1810.2810.4200.1920.1200.1990.177-0.034-0.3330.528
Tot_Closed_TL0.217-0.1340.6410.1760.0200.1400.1450.0930.0780.1220.2620.2620.0080.0000.0240.5580.2030.0860.0420.1300.3810.0510.2880.2110.1070.0760.2070.0040.6220.1010.2841.0000.1950.6550.5170.8010.2570.1930.3800.0930.0640.0760.0000.0000.3110.0940.0450.3030.1650.019-0.0020.0000.1390.1370.100-0.0020.0000.0000.3520.2540.2070.0350.0020.0000.2900.2510.3060.0800.0770.0590.046-0.7800.7800.024-0.7800.0630.5250.449-0.288-0.0300.2890.0000.3210.302-0.0040.1020.288
Tot_Missed_Pmnt0.028-0.5190.0650.0790.0320.0840.1010.1050.1030.0900.1930.0270.0000.0000.0270.2280.1500.0620.0000.0750.3350.0610.1590.1600.1440.1360.1740.0180.2680.0260.5300.1951.0000.2180.2020.4220.5020.5100.2750.2150.1820.2140.0000.0000.0590.1290.1640.0590.260-0.009-0.0200.0000.0340.0230.043-0.0200.0000.0000.2160.2330.2340.008-0.0060.0000.0650.0690.0570.1130.1130.1310.1290.092-0.0920.4000.0920.3960.0960.1380.2340.4000.0510.0460.0570.043-0.095-0.1960.206
Tot_TL_closed_L12M0.039-0.3210.2090.0800.1580.1310.1860.1740.1570.1300.4010.0700.0320.0160.0320.2610.1910.0340.0320.0710.2960.2010.2490.2610.2080.1660.2050.0030.283-0.0040.3200.6550.2181.0000.8050.5650.4480.3230.4300.2190.1460.1810.0310.0460.1930.2510.1930.1900.2010.004-0.0020.0000.1610.1610.1030.0070.0000.0000.2300.2300.171-0.0110.0060.0000.1490.1270.1960.1240.1220.1220.114-0.4490.4490.088-0.4490.1660.9330.7660.0390.1410.1800.0930.1880.179-0.014-0.0750.317
Tot_TL_closed_L6M0.012-0.3290.1270.0560.1540.1290.1770.1770.1580.1320.4030.0290.0270.0290.0070.1630.1280.0050.0190.0600.2450.2110.2200.2540.2120.1800.1840.0020.178-0.0170.2870.5170.2020.8051.0000.4590.4380.3530.4010.2330.1510.1940.0440.0480.1160.2160.2160.1150.184-0.000-0.0020.0000.1300.1250.085-0.0050.0000.0000.1400.1520.141-0.0190.0030.0000.0730.0530.1190.1290.1290.1400.133-0.3360.3360.053-0.3360.1990.7370.9740.1060.1890.1080.0830.1110.107-0.020-0.1170.297
Total_TL0.168-0.4440.5260.2150.0710.2840.3060.2520.2280.2680.4310.1960.0150.0000.0550.5070.2200.1310.0490.1810.4940.0950.3680.3600.2620.2190.3210.0090.6200.0790.7400.8010.4220.5650.4591.0000.5770.4620.6220.3080.2060.2600.0090.0000.3200.2820.2760.3160.4680.006-0.0040.0000.2010.1800.163-0.0050.0000.0000.3820.3220.2900.0340.0000.0000.2760.2340.3220.1950.1920.1660.154-0.3450.3450.252-0.3450.2900.3710.360-0.0220.2320.3010.0430.3250.302-0.027-0.0990.484
Total_TL_opened_L12M-0.064-0.830-0.0890.0670.1970.2320.2960.3270.2920.2390.506-0.0710.0300.0230.0400.1760.1460.0260.0000.1050.3140.2490.2620.3760.3840.3250.2770.0130.184-0.0480.7320.2570.5020.4480.4380.5771.0000.7550.5600.5400.3190.4270.0290.0610.0430.3060.3860.0440.479-0.022-0.0140.0000.0860.0400.102-0.0340.0000.0000.1210.1760.187-0.029-0.0250.0000.0220.0210.0460.2500.2510.2720.2720.140-0.1400.4140.1400.5820.3060.3650.7030.6250.0360.1110.0330.028-0.122-0.3420.452
Total_TL_opened_L6M-0.072-0.846-0.0640.0480.1720.1920.2440.2680.2700.1980.402-0.1130.0270.0100.0000.1400.1230.0020.0000.0820.2660.2500.2290.3210.3330.3320.2370.0220.140-0.0440.5980.1930.5100.3230.3530.4620.7551.0000.4520.4480.3200.4680.0270.0620.0000.1770.2560.0010.393-0.009-0.0080.000-0.001-0.0360.032-0.0220.0000.0000.0920.1270.141-0.018-0.0180.0000.0060.006-0.0010.2610.2620.3110.3120.124-0.1240.3780.1240.9140.1990.2830.4890.922-0.0040.1450.001-0.006-0.140-0.2790.382
Unsecured_TL0.059-0.4560.183-0.0200.3420.4110.4660.4120.3810.3900.755-0.0210.0530.0370.060-0.0130.0560.0470.0520.1730.3060.3910.4850.5490.4430.3790.435-0.005-0.094-0.0070.6620.3800.2750.4300.4010.6220.5600.4521.0000.4680.3240.3990.0720.0830.1480.3510.3670.1460.792-0.0000.0090.0000.1820.1600.141-0.0030.0000.0000.0920.0840.0790.009-0.0080.0000.0850.0680.1610.2830.2800.2790.268-0.0190.0190.112-0.0190.3090.3120.3390.1980.3100.1400.1220.1470.1340.013-0.2060.599
enq_L12m-0.144-0.487-0.1130.0950.2500.2770.5940.6570.6400.2900.453-0.4490.0410.0340.000-0.0270.0000.0120.0490.1330.0630.2380.2590.6790.7370.6920.2670.005-0.021-0.1270.4320.0930.2150.2190.2330.3080.5400.4480.4681.0000.8090.9080.0590.0820.0920.3010.3420.0920.402-0.0060.0070.0000.1300.1040.111-0.0050.0000.000-0.083-0.056-0.046-0.0020.0000.0000.0510.0340.1040.4150.4160.5090.5120.140-0.1400.1890.1400.3550.1520.1990.4280.4010.0860.2070.0870.080-0.187-0.2800.833
enq_L3m-0.113-0.342-0.0630.0740.1510.1950.5220.5740.6030.2040.309-0.5140.0300.0090.019-0.0260.031-0.0040.0640.1050.0370.1590.1860.5590.6060.6430.1820.015-0.026-0.1060.2830.0640.1820.1460.1510.2060.3190.3200.3240.8091.0000.8930.0540.1070.0900.2130.2300.0900.268-0.0010.0090.0000.1090.0950.0900.0070.0000.000-0.067-0.047-0.0430.0030.0040.0000.0530.0430.1000.3470.3470.4470.4470.089-0.0890.1200.0890.2670.1000.1260.2350.2960.0840.1690.0860.080-0.274-0.1920.689
enq_L6m-0.136-0.447-0.0920.0840.1990.2350.5560.6150.6430.2470.383-0.4940.0300.0250.000-0.0240.0160.0040.0600.1120.0530.2000.2270.6360.6910.7300.2270.009-0.023-0.1200.3640.0760.2140.1810.1940.2600.4270.4680.3990.9080.8931.0000.0490.0760.0880.2470.2770.0870.337-0.0010.0060.0000.1080.0870.0920.0040.0000.000-0.074-0.051-0.0440.0030.0050.0000.0550.0400.0980.4160.4170.5630.5640.118-0.1180.1630.1180.4120.1240.1640.3230.4430.0800.2260.0850.077-0.244-0.2370.766
first_prod_enq20.0750.0570.1060.0670.4710.1090.1390.1390.1060.2010.0740.0850.0680.0680.4330.0000.0390.2010.0890.0160.0260.2680.0910.0980.0520.0350.1061.0000.0080.0580.0450.0000.0000.0310.0440.0090.0290.0270.0720.0590.0540.0491.0000.3330.0320.0350.0260.0320.0090.0070.0000.0000.0590.0500.0530.0000.0000.0000.0470.0630.0560.0060.0150.0210.0440.0340.0600.1050.1170.1020.1090.0780.0790.0280.0780.0520.0800.0690.0790.0610.0320.0770.0640.0490.0590.0560.077
last_prod_enq20.0500.0780.0510.0230.4420.0690.1450.2320.2210.1880.0700.1100.0670.0590.2750.0000.0530.1270.0920.0350.0060.2540.0450.1040.0760.0640.1081.0000.0000.0500.0660.0000.0000.0460.0480.0000.0610.0620.0830.0820.1070.0760.3331.0000.0190.0220.0100.0110.0000.0160.0060.0000.0300.0250.0320.0000.0000.0000.0190.0270.0210.0000.0000.0000.0030.0080.0170.2210.2200.2320.2320.0760.0780.0000.0760.0790.0700.0660.0970.0830.0180.0860.0490.0430.1160.0700.100
max_delinquency_level0.0740.0000.2980.1820.0480.0990.1290.0870.0870.0900.056-0.2010.0380.0200.0720.2250.1530.1270.0570.0810.1630.0020.0900.0950.0540.0610.059-0.0080.2980.0270.2020.3110.0590.1930.1160.3200.0430.0000.1480.0920.0900.0880.0320.0191.0000.5270.3450.9880.1020.036-0.0010.0000.6630.5730.4950.0210.0000.0000.1480.1150.0950.0410.0190.0700.7750.6250.9830.0400.0360.0140.006-0.1830.1830.038-0.183-0.0320.1370.089-0.184-0.0880.9820.0080.9770.9600.0040.0050.203
max_deliq_12mts-0.028-0.287-0.0240.1650.0700.2220.3030.2690.2580.2170.312-0.1630.0500.0080.058-0.0200.0350.1090.0380.0950.0950.0230.1400.2590.2340.2210.132-0.0020.064-0.0400.3950.0940.1290.2510.2160.2820.3060.1770.3510.3010.2130.2470.0350.0220.5271.0000.8380.5300.278-0.009-0.0140.0000.7710.6560.577-0.0190.0000.000-0.099-0.079-0.078-0.038-0.0220.1060.3340.2270.5560.1300.1280.1140.1080.155-0.1550.1470.1550.1390.2170.2010.1890.1290.5240.0150.4830.4340.052-0.2290.370
max_deliq_6mts-0.048-0.374-0.0800.1790.0600.2540.3340.3010.2850.2480.348-0.1430.0290.0210.060-0.0560.0320.0930.0340.1010.0750.0000.1480.2860.2710.2500.144-0.0040.040-0.0320.4410.0450.1640.1930.2160.2760.3860.2560.3670.3420.2300.2770.0260.0100.3450.8381.0000.3470.301-0.034-0.0170.0000.5380.3480.645-0.0320.0000.000-0.157-0.132-0.130-0.057-0.0360.0770.2110.1320.3780.1530.1510.1380.1330.240-0.2400.1830.2400.2200.1370.1980.2980.2200.3390.0000.3160.2550.058-0.3000.392
max_recent_level_of_deliq0.075-0.0010.2930.1880.0600.0950.1250.0870.0880.0880.059-0.1980.0510.0000.0600.2160.0820.1290.0290.0830.1580.0350.0930.0980.0550.0620.061-0.0080.2940.0290.2030.3030.0590.1900.1150.3160.0440.0010.1460.0920.0900.0870.0320.0110.9880.5300.3471.0000.1020.037-0.0010.0000.6640.5690.4940.0220.0000.0000.1440.1140.0950.0380.0180.0720.7200.5730.9720.0400.0360.0140.005-0.1760.1760.035-0.176-0.0320.1350.088-0.181-0.0870.9930.0000.9720.9590.0070.0020.203
max_unsec_exposure_inPct0.042-0.4150.120-0.0400.0130.3990.4090.3530.3200.3970.4920.0060.0000.0230.000-0.0090.0030.0210.0000.0750.2520.0130.4980.4840.3940.3210.541-0.001-0.097-0.0240.6510.1650.2600.2010.1840.4680.4790.3930.7920.4020.2680.3370.0090.0000.1020.2780.3010.1021.000-0.0050.0050.0000.1410.1180.1160.0040.0000.0000.1540.1760.1790.0370.0080.0000.0630.0430.1150.2500.2470.2480.2360.203-0.2030.2210.2030.2970.0770.1180.2200.2980.0960.0000.1020.086-0.007-0.2470.478
num_dbt0.0240.0330.070-0.0050.000-0.007-0.012-0.009-0.009-0.006-0.0210.0240.0470.0000.0000.0080.0000.0080.0130.0160.0580.000-0.007-0.004-0.006-0.002-0.0080.0100.0190.010-0.0070.019-0.0090.004-0.0000.006-0.022-0.009-0.000-0.006-0.001-0.0010.0070.0160.036-0.009-0.0340.037-0.0051.0000.5310.6610.0120.017-0.008-0.0020.0000.0000.0280.0020.0010.1160.0290.0000.0360.0520.035-0.010-0.0100.001-0.001-0.0240.024-0.010-0.024-0.014-0.000-0.003-0.042-0.0190.0340.0000.0340.030-0.0090.035-0.006
num_dbt_12mts0.0030.0200.035-0.0110.0000.004-0.0000.0030.0050.004-0.0030.0050.0540.0230.015-0.0060.0000.0060.0050.0100.0230.000-0.0040.002-0.0010.002-0.0020.001-0.005-0.0050.000-0.002-0.020-0.002-0.002-0.004-0.014-0.0080.0090.0070.0090.0060.0000.006-0.001-0.014-0.017-0.0010.0050.5311.0000.866-0.005-0.003-0.000-0.0010.0000.0000.012-0.007-0.0040.0540.0570.0000.0050.012-0.004-0.002-0.002-0.002-0.0030.008-0.008-0.0070.008-0.011-0.003-0.003-0.022-0.012-0.0030.000-0.003-0.0060.0020.0210.007
num_dbt_6mts0.0000.1010.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6610.8661.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.000
num_deliq_12mts0.032-0.0350.1270.1250.1500.1470.1610.1230.1080.1420.101-0.0820.0220.0140.1160.0470.0280.1270.0520.0680.1160.0850.1030.1180.0770.0770.0820.0020.1210.0040.2030.1390.0340.1610.1300.2010.086-0.0010.1820.1300.1090.1080.0590.0300.6630.7710.5380.6640.1410.012-0.0050.0001.0000.8570.7640.0040.0000.0000.0630.0580.0580.0020.0030.0000.4240.2840.7100.0620.0580.0320.022-0.0230.0230.048-0.023-0.0280.1320.117-0.047-0.0530.6540.0630.6110.542-0.023-0.0900.204
num_deliq_6_12mts0.0340.0030.1330.1050.1140.1300.1450.1010.0920.1260.089-0.0810.0160.0000.1120.0380.0320.1190.0490.0630.0970.0800.0940.1060.0650.0670.0730.0050.1070.0010.1600.1370.0230.1610.1250.1800.040-0.0360.1600.1040.0950.0870.0500.0250.5730.6560.3480.5690.1180.017-0.0030.0000.8571.0000.4610.0100.0000.0000.0650.0550.0530.0060.0040.0000.4100.2870.6200.0500.0460.0280.018-0.0470.0470.020-0.047-0.0600.1400.115-0.085-0.0840.5550.0680.5350.478-0.011-0.0570.184
num_deliq_6mts0.031-0.0570.0990.1230.1530.1470.1410.1040.0850.1420.069-0.0290.0120.0050.1050.0290.0460.1160.0360.0620.0940.0770.0860.0980.0650.0610.066-0.0000.1060.0250.1850.1000.0430.1030.0850.1630.1020.0320.1410.1110.0900.0920.0530.0320.4950.5770.6450.4940.116-0.008-0.0000.0000.7640.4611.000-0.0070.0000.0000.0460.0450.044-0.0130.0030.0270.3370.2260.5440.0540.0500.0280.0210.008-0.0080.0620.0080.0120.0690.0700.003-0.0060.4810.0380.4520.352-0.033-0.1030.163
num_lss-0.0070.0360.044-0.0030.000-0.015-0.0010.0040.008-0.013-0.0180.0040.0000.0090.000-0.0100.0000.0180.000-0.0160.0320.0000.0070.0050.0050.0130.0060.0020.0030.018-0.003-0.002-0.0200.007-0.005-0.005-0.034-0.022-0.003-0.0050.0070.0040.0000.0000.021-0.019-0.0320.0220.004-0.002-0.0010.0000.0040.010-0.0071.0000.6450.6450.0260.0130.0020.0540.0770.0000.0320.0220.0190.0100.0110.0180.0160.005-0.005-0.0050.005-0.0210.006-0.007-0.040-0.0250.0200.0000.0180.017-0.0030.0170.002
num_lss_12mts0.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0060.0000.0060.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6451.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0360.0420.0400.0000.1170.000
num_lss_6mts0.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0060.0000.0060.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6451.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0360.0420.0400.0000.1170.000
num_std0.172-0.0790.3730.0580.022-0.005-0.048-0.064-0.065-0.013-0.1410.4020.0180.0000.1260.3620.2650.1380.0830.0650.4060.1010.155-0.000-0.069-0.0780.0990.0010.4350.1110.2700.3520.2160.2300.1400.3820.1210.0920.092-0.083-0.067-0.0740.0470.0190.148-0.099-0.1570.1440.1540.0280.0120.0000.0630.0650.0460.0260.0000.0001.0000.8800.8140.1270.0570.0000.1570.1530.144-0.007-0.009-0.047-0.055-0.1700.1700.271-0.1700.0480.1410.094-0.162-0.0130.1350.0140.1500.135-0.041-0.001-0.007
num_std_12mts0.131-0.1420.2520.0510.020-0.005-0.034-0.046-0.045-0.009-0.1340.3880.0240.0070.1370.3120.2920.1370.0870.0610.3470.1120.1540.016-0.044-0.0530.1070.0020.3700.0960.2950.2540.2330.2300.1520.3220.1760.1270.084-0.056-0.047-0.0510.0630.0270.115-0.079-0.1320.1140.1760.002-0.0070.0000.0580.0550.0450.0130.0000.0000.8801.0000.9450.0800.0500.0000.1240.1180.1090.002-0.000-0.029-0.036-0.0630.0630.320-0.0630.0860.1530.115-0.0590.0410.1060.0320.1130.100-0.041-0.0600.000
num_std_6mts0.118-0.1560.2140.0460.000-0.009-0.035-0.046-0.046-0.015-0.1250.3720.0170.0250.1370.2830.2710.1310.0760.0590.3100.0980.1480.017-0.036-0.0440.1090.0040.3350.0910.2970.2070.2340.1710.1410.2900.1870.1410.079-0.046-0.043-0.0440.0560.0210.095-0.078-0.1300.0950.1790.001-0.0040.0000.0580.0530.0440.0020.0000.0000.8140.9451.0000.0670.0390.0000.1050.0990.091-0.004-0.006-0.021-0.028-0.0190.0190.323-0.0190.1030.0970.108-0.0220.0630.0880.0000.0920.080-0.040-0.0800.002
num_sub0.0300.0370.1060.0010.009-0.031-0.007-0.0030.003-0.029-0.0470.0540.0110.0000.0340.0480.0330.0210.0180.0070.0830.000-0.0020.003-0.008-0.010-0.0170.0030.0470.0190.0120.0350.008-0.011-0.0190.034-0.029-0.0180.009-0.0020.0030.0030.0060.0000.041-0.038-0.0570.0380.0370.1160.0540.0000.0020.006-0.0130.0540.0000.0000.1270.0800.0671.0000.4820.5800.0520.0750.0400.0080.008-0.007-0.008-0.0210.0210.022-0.021-0.017-0.018-0.021-0.062-0.0320.0370.0270.0390.034-0.0130.0400.001
num_sub_12mts0.0120.0230.038-0.0030.000-0.0080.0000.0040.005-0.007-0.0260.0180.0000.0000.000-0.0010.0000.0080.011-0.0060.0360.000-0.012-0.012-0.016-0.012-0.017-0.0130.0100.012-0.0070.002-0.0060.0060.0030.000-0.025-0.018-0.0080.0000.0040.0050.0150.0000.019-0.022-0.0360.0180.0080.0290.0570.0000.0030.0040.0030.0770.0000.0000.0570.0500.0390.4821.0000.6710.0290.0400.0150.0100.010-0.013-0.0150.004-0.0040.0120.004-0.0160.0050.004-0.031-0.0220.0180.0250.0120.011-0.0060.0170.002
num_sub_6mts0.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0700.1060.0770.0720.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.5800.6711.0000.0650.0790.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0350.0000.0040.0000.0820.000
num_times_30p_dpd0.0830.0050.2820.1290.0610.0590.0750.0320.0360.046-0.007-0.1160.0000.0310.0660.2420.1300.1140.0570.0690.1600.0430.0590.0580.0290.0380.034-0.0090.2910.0390.1440.2900.0650.1490.0730.2760.0220.0060.0850.0510.0530.0550.0440.0030.7750.3340.2110.7200.0630.0360.0050.0000.4240.4100.3370.0320.0000.0000.1570.1240.1050.0520.0290.0651.0000.7900.7320.0120.0080.0170.010-0.1880.1880.042-0.188-0.0150.0910.047-0.167-0.0660.6970.0310.7080.638-0.0070.0370.135
num_times_60p_dpd0.0770.0040.2490.0890.0550.0420.0490.0160.0170.028-0.017-0.0810.0000.0280.0600.2260.0950.0880.0510.0660.1530.0310.0410.0320.0130.0190.012-0.0100.2500.0390.1120.2510.0690.1270.0530.2340.0210.0060.0680.0340.0430.0400.0340.0080.6250.2270.1320.5730.0430.0520.0120.0000.2840.2870.2260.0220.0000.0000.1530.1180.0990.0750.0400.0790.7901.0000.5750.002-0.0010.0040.000-0.1650.1650.052-0.165-0.0110.0730.029-0.134-0.0500.5460.0130.5560.490-0.0150.0410.097
num_times_delinquent0.074-0.0010.2980.1950.0920.1180.1490.1040.1020.1090.064-0.1880.0000.0320.1190.2070.1210.1320.0650.0870.1600.0890.1040.1110.0650.0700.073-0.0070.2900.0290.2130.3060.0570.1960.1190.3220.046-0.0010.1610.1040.1000.0980.0600.0170.9830.5560.3780.9720.1150.035-0.0040.0000.7100.6200.5440.0190.0000.0000.1440.1090.0910.0400.0150.0090.7320.5751.0000.0520.0470.0190.009-0.1740.1740.038-0.174-0.0340.1390.091-0.182-0.0890.9640.0480.9790.9460.004-0.0100.221
pct_CC_enq_L6m_of_L12m-0.075-0.2330.0180.0740.4710.4560.6080.6940.7670.4620.165-0.1720.0600.0390.0470.0060.0230.0500.0690.1150.0230.2180.2140.3200.3080.2810.1940.0060.016-0.0810.2600.0800.1130.1240.1290.1950.2500.2610.2830.4150.3470.4160.1050.2210.0400.1300.1530.0400.250-0.010-0.0020.0000.0620.0500.0540.0100.0000.000-0.0070.002-0.0040.0080.0100.0000.0120.0020.0521.0000.9990.2310.2280.057-0.0570.1110.0570.2030.0680.0980.1390.2160.0350.2200.0480.038-0.113-0.1750.404
pct_CC_enq_L6m_of_ever-0.078-0.2340.0130.0720.4750.4500.6060.6940.7670.4580.164-0.1750.0600.0420.0690.0040.0520.0480.0530.1130.0210.2300.2110.3180.3080.2810.1910.0070.014-0.0820.2570.0770.1130.1220.1290.1920.2510.2620.2800.4160.3470.4170.1170.2200.0360.1280.1510.0360.247-0.010-0.0020.0000.0580.0460.0500.0110.0000.000-0.009-0.000-0.0060.0080.0100.0000.008-0.0010.0470.9991.0000.2320.2290.059-0.0590.1110.0590.2050.0670.0980.1430.2190.0310.2180.0430.034-0.113-0.1740.402
pct_PL_enq_L6m_of_L12m-0.106-0.275-0.0620.0070.1820.1210.2660.2820.2860.1280.256-0.3850.0470.0410.000-0.0100.000-0.0130.0740.0550.0240.3120.2390.6460.7450.8630.244-0.010-0.044-0.0930.2240.0590.1310.1220.1400.1660.2720.3110.2790.5090.4470.5630.1020.2320.0140.1140.1380.0140.2480.001-0.0020.0000.0320.0280.0280.0180.0000.000-0.047-0.029-0.021-0.007-0.0130.0000.0170.0040.0190.2310.2321.0000.9940.055-0.0550.0740.0550.2680.0920.1240.1980.2870.0100.1760.0150.009-0.159-0.1280.438
pct_PL_enq_L6m_of_ever-0.116-0.276-0.0780.0020.2120.1110.2580.2780.2840.1190.253-0.3940.0450.0380.039-0.0150.044-0.0180.0870.0490.0220.3630.2210.6380.7460.8630.227-0.009-0.050-0.0970.2160.0460.1290.1140.1330.1540.2720.3120.2680.5120.4470.5640.1090.2320.0060.1080.1330.0050.236-0.001-0.0030.0000.0220.0180.0210.0160.0000.000-0.055-0.036-0.028-0.008-0.0150.0000.0100.0000.0090.2280.2290.9941.0000.062-0.0620.0730.0620.2700.0870.1190.2090.2930.0010.1700.0060.002-0.159-0.1240.430
pct_active_tl-0.186-0.227-0.532-0.0580.2730.0700.0740.0950.0900.083-0.019-0.2070.0450.0290.111-0.3820.452-0.0060.146-0.029-0.1320.322-0.0750.0210.0810.0800.0020.002-0.387-0.0800.267-0.7800.092-0.449-0.336-0.3450.1400.124-0.0190.1400.0890.1180.0780.076-0.1830.1550.240-0.1760.203-0.0240.0080.000-0.023-0.0470.0080.0050.0000.000-0.170-0.063-0.019-0.0210.0040.000-0.188-0.165-0.1740.0570.0590.0550.0621.000-1.0000.2911.0000.171-0.475-0.3480.4540.249-0.1700.076-0.195-0.191-0.022-0.3240.006
pct_closed_tl0.1860.2270.5320.0580.270-0.070-0.074-0.095-0.090-0.0830.0190.2070.0460.0340.1170.3820.4610.0060.1490.0290.1320.3170.075-0.021-0.081-0.080-0.002-0.0020.3870.080-0.2670.780-0.0920.4490.3360.345-0.140-0.1240.019-0.140-0.089-0.1180.0790.0780.183-0.155-0.2400.176-0.2030.024-0.0080.0000.0230.047-0.008-0.0050.0000.0000.1700.0630.0190.021-0.0040.0000.1880.1650.174-0.057-0.059-0.055-0.062-1.0001.000-0.291-1.000-0.1710.4750.348-0.454-0.2490.1700.0780.1950.1910.0220.324-0.006
pct_currentBal_all_TL-0.036-0.496-0.0740.0330.0210.1300.1030.0940.0790.150-0.0570.0450.0080.0360.0200.1590.0160.1410.0000.0710.2230.0000.1310.0900.0880.0680.1640.0070.227-0.0160.4160.0240.4000.0880.0530.2520.4140.3780.1120.1890.1200.1630.0280.0000.0380.1470.1830.0350.221-0.010-0.0070.0000.0480.0200.062-0.0050.0000.0000.2710.3200.3230.0220.0120.0000.0420.0520.0380.1110.1110.0740.0730.291-0.2911.0000.2910.388-0.0030.0080.3540.3800.0290.0000.0290.020-0.128-0.2860.136
pct_of_active_TLs_ever-0.186-0.227-0.532-0.0580.2730.0700.0740.0950.0900.083-0.019-0.2070.0450.0290.111-0.3820.452-0.0060.146-0.029-0.1320.322-0.0750.0210.0810.0800.0020.002-0.387-0.0800.267-0.7800.092-0.449-0.336-0.3450.1400.124-0.0190.1400.0890.1180.0780.076-0.1830.1550.240-0.1760.203-0.0240.0080.000-0.023-0.0470.0080.0050.0000.000-0.170-0.063-0.019-0.0210.0040.000-0.188-0.165-0.1740.0570.0590.0550.0621.000-1.0000.2911.0000.171-0.475-0.3480.4540.249-0.1700.076-0.195-0.191-0.022-0.3240.006
pct_opened_TLs_L6m_of_L12m-0.088-0.812-0.139-0.0040.2090.1250.1720.1930.2090.1320.271-0.1260.0300.0290.0370.0780.161-0.0160.0540.0500.1550.2410.1450.2290.2500.2720.1560.0170.059-0.0470.4250.0630.3960.1660.1990.2900.5820.9140.3090.3550.2670.4120.0520.079-0.0320.1390.220-0.0320.297-0.014-0.0110.000-0.028-0.0600.012-0.0210.0000.0000.0480.0860.103-0.017-0.0160.000-0.015-0.011-0.0340.2030.2050.2680.2700.171-0.1710.3880.1711.0000.0950.1610.4870.960-0.0350.107-0.031-0.038-0.141-0.2560.273
pct_tl_closed_L12M-0.003-0.2180.0930.0140.1890.0440.1120.1150.1080.0470.3300.0180.0290.0390.0810.1380.220-0.0020.0590.0190.1700.2510.1630.1850.1570.1280.124-0.0050.138-0.0280.1170.5250.0960.9330.7370.3710.3060.1990.3120.1520.1000.1240.0800.0700.1370.2170.1370.1350.077-0.000-0.0030.0000.1320.1400.0690.0060.0000.0000.1410.1530.097-0.0180.0050.0000.0910.0730.1390.0680.0670.0920.087-0.4750.475-0.003-0.4750.0951.0000.7670.0390.0830.1310.0770.1300.1290.006-0.0180.218
pct_tl_closed_L6M-0.009-0.2780.0640.0190.1650.0800.1370.1440.1320.0840.3630.0050.0230.0440.0530.1010.169-0.0110.0470.0320.1770.2100.1710.2120.1850.1600.137-0.0010.104-0.0300.1810.4490.1380.7660.9740.3600.3650.2830.3390.1990.1260.1640.0690.0660.0890.2010.1980.0880.118-0.003-0.0030.0000.1170.1150.070-0.0070.0040.0040.0940.1150.108-0.0210.0040.0000.0470.0290.0910.0980.0980.1240.119-0.3480.3480.008-0.3480.1610.7671.0000.1140.1590.0830.0670.0810.082-0.010-0.0900.244
pct_tl_open_L12M-0.207-0.683-0.577-0.1180.1920.0480.1240.1990.1790.0670.270-0.2550.0360.0430.106-0.1490.329-0.0630.165-0.012-0.0320.2610.0220.1840.2650.2270.0610.009-0.232-0.1190.281-0.2880.2340.0390.106-0.0220.7030.4890.1980.4280.2350.3230.0790.097-0.1840.1890.298-0.1810.220-0.042-0.0220.000-0.047-0.0850.003-0.0400.0000.000-0.162-0.059-0.022-0.062-0.0310.000-0.167-0.134-0.1820.1390.1430.1980.2090.454-0.4540.3540.4540.4870.0390.1141.0000.585-0.1800.086-0.200-0.193-0.134-0.3470.184
pct_tl_open_L6M-0.130-0.813-0.252-0.0360.1950.1150.1750.2140.2280.1260.298-0.1900.0320.0210.0890.0100.230-0.0350.1200.0370.1240.2330.1300.2400.2810.2960.1480.018-0.024-0.0720.420-0.0300.4000.1410.1890.2320.6250.9220.3100.4010.2960.4430.0610.083-0.0880.1290.220-0.0870.298-0.019-0.0120.000-0.053-0.084-0.006-0.0250.0000.000-0.0130.0410.063-0.032-0.0220.000-0.066-0.050-0.0890.2160.2190.2870.2930.249-0.2490.3800.2490.9600.0830.1590.5851.000-0.0880.091-0.090-0.092-0.149-0.2810.279
recent_level_of_deliq0.0710.0050.2830.1800.0550.0860.1170.0810.0830.0790.058-0.2070.0190.0110.0360.2040.0440.1240.0270.0760.1500.0000.0860.0890.0490.0570.056-0.0070.2800.0260.1920.2890.0510.1800.1080.3010.036-0.0040.1400.0860.0840.0800.0320.0180.9820.5240.3390.9930.0960.034-0.0030.0000.6540.5550.4810.0200.0000.0000.1350.1060.0880.0370.0180.0880.6970.5460.9640.0350.0310.0100.001-0.1700.1700.029-0.170-0.0350.1310.083-0.180-0.0881.0000.0000.9670.9630.0100.0080.193
response_flag0.0290.0220.0070.0360.1690.0860.1960.2420.2640.1930.0810.1360.0360.0000.0040.0260.0120.0000.0000.0000.0000.1340.0850.2110.2230.2160.1561.0000.0180.0000.1200.0000.0460.0930.0830.0430.1110.1450.1220.2070.1690.2260.0770.0860.0080.0150.0000.0000.0000.0000.0000.0000.0630.0680.0380.0000.0360.0360.0140.0320.0000.0270.0250.0350.0310.0130.0480.2200.2180.1760.1700.0760.0780.0000.0760.1070.0770.0670.0860.0910.0001.0000.0060.0000.0350.0290.216
time_since_first_deliquency0.0750.0060.3130.1950.1070.1060.1350.0910.0920.0980.054-0.2040.0200.0470.1230.2310.2360.1210.0790.0850.1550.0970.0990.1010.0560.0620.068-0.0080.3050.0300.1990.3210.0570.1880.1110.3250.0330.0010.1470.0870.0860.0850.0640.0490.9770.4830.3160.9720.1020.034-0.0030.0000.6110.5350.4520.0180.0420.0420.1500.1130.0920.0390.0120.0000.7080.5560.9790.0480.0430.0150.006-0.1950.1950.029-0.195-0.0310.1300.081-0.200-0.0900.9670.0061.0000.9760.0110.0090.207
time_since_recent_deliquency0.0620.0110.2850.1730.0740.0830.1160.0810.0830.0770.058-0.2300.0080.0240.1070.2220.2170.1020.0400.0690.1360.0780.0800.0860.0480.0550.054-0.0090.2830.0190.1770.3020.0430.1790.1070.3020.028-0.0060.1340.0800.0800.0770.0490.0430.9600.4340.2550.9590.0860.030-0.0060.0000.5420.4780.3520.0170.0400.0400.1350.1000.0800.0340.0110.0040.6380.4900.9460.0380.0340.0090.002-0.1910.1910.020-0.191-0.0380.1290.082-0.193-0.0920.9630.0000.9761.0000.0130.0200.186
time_since_recent_enq0.0790.1420.0700.1350.0810.0060.2330.2340.263-0.0070.0210.2930.0090.0360.002-0.0990.0530.0300.0740.027-0.0690.0670.0080.1300.1100.126-0.009-0.025-0.0270.080-0.034-0.004-0.095-0.014-0.020-0.027-0.122-0.1400.013-0.187-0.274-0.2440.0590.1160.0040.0520.0580.007-0.007-0.0090.0020.000-0.023-0.011-0.033-0.0030.0000.000-0.041-0.041-0.040-0.013-0.0060.000-0.007-0.0150.004-0.113-0.113-0.159-0.159-0.0220.022-0.128-0.022-0.1410.006-0.010-0.134-0.1490.0100.0350.0110.0131.0000.105-0.031
time_since_recent_payment0.1050.3910.212-0.0950.119-0.264-0.233-0.204-0.181-0.261-0.1380.1050.0230.0140.0460.0640.037-0.0450.069-0.056-0.0460.092-0.135-0.172-0.167-0.155-0.156-0.0170.0150.056-0.3330.102-0.196-0.075-0.117-0.099-0.342-0.279-0.206-0.280-0.192-0.2370.0560.0700.005-0.229-0.3000.002-0.2470.0350.0210.083-0.090-0.057-0.1030.0170.1170.117-0.001-0.060-0.0800.0400.0170.0820.0370.041-0.010-0.175-0.174-0.128-0.124-0.3240.324-0.286-0.324-0.256-0.018-0.090-0.347-0.2810.0080.0290.0090.0200.1051.000-0.244
tot_enq-0.030-0.3910.1350.2450.3960.3840.6740.6470.6310.3770.520-0.2700.0690.0610.107-0.0030.0350.1000.0220.2150.1290.3680.3800.7390.6750.6370.3510.0050.098-0.0560.5280.2880.2060.3170.2970.4840.4520.3820.5990.8330.6890.7660.0770.1000.2030.3700.3920.2030.478-0.0060.0070.0000.2040.1840.1630.0020.0000.000-0.0070.0000.0020.0010.0020.0000.1350.0970.2210.4040.4020.4380.4300.006-0.0060.1360.0060.2730.2180.2440.1840.2790.1930.2160.2070.186-0.031-0.2441.000

Missing values

Train

2025-03-10T23:50:03.013983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.

Test

2025-03-10T23:50:13.563089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.

Train

2025-03-10T23:50:03.429398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Test

2025-03-10T23:50:13.939525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Train

PROSPECTIDcampaign_idresponse_flagdirect_mail_flagtime_since_recent_paymenttime_since_first_deliquencytime_since_recent_deliquencynum_times_delinquentmax_delinquency_levelmax_recent_level_of_deliqnum_deliq_6mtsnum_deliq_12mtsnum_deliq_6_12mtsmax_deliq_6mtsmax_deliq_12mtsnum_times_30p_dpdnum_times_60p_dpdnum_stdnum_std_6mtsnum_std_12mtsnum_subnum_sub_6mtsnum_sub_12mtsnum_dbtnum_dbt_6mtsnum_dbt_12mtsnum_lssnum_lss_6mtsnum_lss_12mtsrecent_level_of_deliqtot_enqCC_enqCC_enq_L6mCC_enq_L12mPL_enqPL_enq_L6mPL_enq_L12mtime_since_recent_enqenq_L12menq_L6menq_L3mMARITALSTATUSEDUCATIONAGEGENDERNETMONTHLYINCOMETime_With_Curr_Emprpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_currentBal_all_TLCC_utilizationCC_FlagPL_utilizationPL_Flagpct_PL_enq_L6m_of_L12mpct_CC_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_CC_enq_L6m_of_evermax_unsec_exposure_inPctHL_FlagGL_Flaglast_prod_enq2first_prod_enq2Credit_ScoreTotal_TLTot_Closed_TLTot_Active_TLTotal_TL_opened_L6MTot_TL_closed_L6Mpct_tl_open_L6Mpct_tl_closed_L6Mpct_active_tlpct_closed_tlTotal_TL_opened_L12MTot_TL_closed_L12Mpct_tl_open_L12Mpct_tl_closed_L12MTot_Missed_PmntAuto_TLCC_TLConsumer_TLGold_TLHome_TLPL_TLSecured_TLUnsecured_TLOther_TLAge_Oldest_TLAge_Newest_TL
21639932464310.0Y71-1-10-10000000084800000000003000111117220MarriedSSC31M280001861.0001.0000.566-1.000.84311.0000.01.0000.00.21400PLothers673202100.5000.0001.0000.000100.5000.0000100001110144
18112727174910.0Y49872592802205910000000000000282000000235200MarriedGRADUATE35F350001260.6670.0000.283-1.00-1.00000.0000.00.0000.00.51000ConsumerLoanothers663312010.0000.3330.6670.333311.0000.333100200003188
22472933721310.0Y29-1-10-10000000000000000000002000000359100MarriedSSC36M250002471.0000.0000.500-1.00-1.00000.0000.00.0000.0-1.00000othersothers688101000.0000.0001.0000.000000.0000.00001000001001515
10845516271010.0Y47-1-10-10000-1-100444000000000020000003221SingleGRADUATE21M12391.0001.0000.948-1.00-1.00000.0000.00.0000.0-1.00000ConsumerLoanothers678101101.0000.0001.0000.000101.0000.000010000010044
37098355644210.0Y48-1-10-10000000030000000000005000100332100MarriedOTHERS32F18000260.7140.3330.660-1.000.97410.0000.00.0000.012.49300ConsumerLoanConsumerLoan682725110.1430.1430.7140.286310.4290.1430105001160456
27695941550610.0Y-1-1-10-1000000000000000000000111100051111SingleUNDER GRADUATE23M12000561.0001.0000.000-1.01-1.00000.0001.00.0001.00.00000CCCC674101101.0000.0001.0000.000101.0000.000001000001022
24798237195711.0Y53-1-10-100000000164100000000000500031348311MarriedGRADUATE33M300001260.9090.7780.782-1.000.89410.3330.00.3330.03.09100PLothers67711110700.6360.0000.9090.091910.8180.09161080011101181
31400747108710.0Y50-1-10-100000000000000000000070000001766MarriedGRADUATE45M258001031.0000.6670.963-1.001.00010.0000.00.0000.04.52700ConsumerLoanothers652303200.6670.0001.0000.000301.0000.000210100112091
14445521666710.0Y42-1-10-10000-1-1001411000000000020000006222MarriedGRADUATE35F30000660.6671.0001.010-1.00-1.00000.0000.00.0000.01.66810ConsumerLoanConsumerLoan683312100.3330.0000.6670.333100.3330.0000000100122542
31968647958610.0Y46-1-10-1000000000000000000000200000058211SingleSSC21M22500681.0000.0000.842-1.00-1.00000.0000.00.0000.0-1.00000ConsumerLoanothers683101000.0000.0001.0000.000101.0000.000010000010077

Test

PROSPECTIDcampaign_idresponse_flagdirect_mail_flagtime_since_recent_paymenttime_since_first_deliquencytime_since_recent_deliquencynum_times_delinquentmax_delinquency_levelmax_recent_level_of_deliqnum_deliq_6mtsnum_deliq_12mtsnum_deliq_6_12mtsmax_deliq_6mtsmax_deliq_12mtsnum_times_30p_dpdnum_times_60p_dpdnum_stdnum_std_6mtsnum_std_12mtsnum_subnum_sub_6mtsnum_sub_12mtsnum_dbtnum_dbt_6mtsnum_dbt_12mtsnum_lssnum_lss_6mtsnum_lss_12mtsrecent_level_of_deliqtot_enqCC_enqCC_enq_L6mCC_enq_L12mPL_enqPL_enq_L6mPL_enq_L12mtime_since_recent_enqenq_L12menq_L6menq_L3mMARITALSTATUSEDUCATIONAGEGENDERNETMONTHLYINCOMETime_With_Curr_Emprpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_currentBal_all_TLCC_utilizationCC_FlagPL_utilizationPL_Flagpct_PL_enq_L6m_of_L12mpct_CC_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_CC_enq_L6m_of_evermax_unsec_exposure_inPctHL_FlagGL_Flaglast_prod_enq2first_prod_enq2Credit_ScoreTotal_TLTot_Closed_TLTot_Active_TLTotal_TL_opened_L6MTot_TL_closed_L6Mpct_tl_open_L6Mpct_tl_closed_L6Mpct_active_tlpct_closed_tlTotal_TL_opened_L12MTot_TL_closed_L12Mpct_tl_open_L12Mpct_tl_closed_L12MTot_Missed_PmntAuto_TLCC_TLConsumer_TLGold_TLHome_TLPL_TLSecured_TLUnsecured_TLOther_TLAge_Oldest_TLAge_Newest_TL
31255446889510.0Y96-1-10-1000000003330000000000-1-1-1-1-1-1-1-1-1-1-1MarriedOTHERS44F180001380.5000.01.023-1.0000-1.00000.00.00.00.0-1.00000othersothers687211010.0000.5000.5000.500110.5000.5001100000201289
35792753688710.0Y479-1-10-10000-1-10030000000000001000000169110Single12TH25M250001160.0000.00.000-1.0000-1.00000.00.00.00.0-1.00000ConsumerLoanConsumerLoan687110000.0000.0000.0001.000000.0000.00000000001011818
11980217971910.0Y639121219090000-1-111100000000000901330200031299MarriedGRADUATE33M24500821.0000.00.923-1.0000-1.00000.00.00.00.0-1.00001ConsumerLoanCC626101000.0000.0001.0000.000000.0000.00000000101004242
23604935407310.0Y300-1-10-10000-1000900000000000050000001111MarriedSSC30M150001020.0000.00.000-1.0000-1.00000.00.00.00.0-1.00000othersothers681330000.0000.0000.0001.000010.0000.33302010002105217
36504054755910.0Y54-1-10-10000000000000000000001000000514000MarriedGRADUATE22M200001301.0000.00.676-1.0000-1.00000.00.00.00.0-1.00000othersothers686101000.0000.0001.0000.000000.0000.00001000001001717
494907416210.0Y28-1-10-10000000000000000000002000200629000Married12TH30M200001891.0000.00.8330.99810.78910.00.00.00.05.04200PLPL689303000.0000.0001.0000.000200.6670.0000110001120199
30490745737510.0Y1807-1-10-10000-1-1000000000000000200000020111MarriedPOST-GRADUATE36F500001250.0000.00.000-1.0000-1.00000.00.00.00.0-1.00000othersAL676110000.0000.0000.0001.000000.0000.00001000001007777
441116605810.0Y52211117727440272721000000000000272200011183771MarriedSSC54F350001260.5831.00.707-1.00001.04211.00.01.00.06.37110othersothers6721257220.1670.1670.5830.417230.1670.2501005302482565
21999333005410.0Y107-1-10-10000000000000000000001000111136110SingleGRADUATE28M25000841.0001.00.600-1.0000-1.00001.00.01.00.00.68000PLPL672101101.0000.0001.0000.000101.0000.000000100001055
31866047802310.0Y77-1-10-1000000000000000000000800044439884SingleGRADUATE21M23000421.0001.00.500-1.0000-1.00001.00.01.00.01.00000PLPL653101101.0000.0001.0000.000101.0000.000000100001055

Train

PROSPECTIDcampaign_idresponse_flagdirect_mail_flagtime_since_recent_paymenttime_since_first_deliquencytime_since_recent_deliquencynum_times_delinquentmax_delinquency_levelmax_recent_level_of_deliqnum_deliq_6mtsnum_deliq_12mtsnum_deliq_6_12mtsmax_deliq_6mtsmax_deliq_12mtsnum_times_30p_dpdnum_times_60p_dpdnum_stdnum_std_6mtsnum_std_12mtsnum_subnum_sub_6mtsnum_sub_12mtsnum_dbtnum_dbt_6mtsnum_dbt_12mtsnum_lssnum_lss_6mtsnum_lss_12mtsrecent_level_of_deliqtot_enqCC_enqCC_enq_L6mCC_enq_L12mPL_enqPL_enq_L6mPL_enq_L12mtime_since_recent_enqenq_L12menq_L6menq_L3mMARITALSTATUSEDUCATIONAGEGENDERNETMONTHLYINCOMETime_With_Curr_Emprpct_of_active_TLs_everpct_opened_TLs_L6m_of_L12mpct_currentBal_all_TLCC_utilizationCC_FlagPL_utilizationPL_Flagpct_PL_enq_L6m_of_L12mpct_CC_enq_L6m_of_L12mpct_PL_enq_L6m_of_everpct_CC_enq_L6m_of_evermax_unsec_exposure_inPctHL_FlagGL_Flaglast_prod_enq2first_prod_enq2Credit_ScoreTotal_TLTot_Closed_TLTot_Active_TLTotal_TL_opened_L6MTot_TL_closed_L6Mpct_tl_open_L6Mpct_tl_closed_L6Mpct_active_tlpct_closed_tlTotal_TL_opened_L12MTot_TL_closed_L12Mpct_tl_open_L12Mpct_tl_closed_L12MTot_Missed_PmntAuto_TLCC_TLConsumer_TLGold_TLHome_TLPL_TLSecured_TLUnsecured_TLOther_TLAge_Oldest_TLAge_Newest_TL
31092146639810.0Y78-1-10-10000000034410000000000030000001462000MarriedUNDER GRADUATE44M300002210.6670.0000.297-1.0000-1.00000.0000.00.0000.015.00000othersothers702312000.0000.00.6670.333000.0000.00000000001237423
33153649735610.0Y71-1-10-1000000000000000000000300000010221Married12TH51M186002201.0001.0000.264-1.0000-1.00000.0000.00.0000.0-1.00000ConsumerLoanAL697202100.5000.01.0000.000100.5000.00001000002011064
31361447047510.0Y99-1-10-100000000000000000000010000002111MarriedOTHERS55M18700671.0001.0000.500-1.0000-1.00000.0000.00.0000.00.66800ConsumerLoanConsumerLoan670101101.0000.01.0000.000101.0000.000000100001055
37727456581010.0Y3198332525231252500000000000000252000000831000MarriedSSC38M13500660.5000.0000.000-1.0000-1.00000.0000.00.0000.0-1.00000ConsumerLoanothers691211000.0000.00.5000.500000.0000.00001010001103528
31089246635010.0Y60-1-10-1000000000000000000000600033390651MarriedSSC39M400001011.0000.8330.752-1.00000.98411.0000.01.0000.04.16000PLothers670606500.8330.01.0000.000601.0000.000100100106483
27618841431910.0Y-1-1-10-10000-1-10095900000000001000000581000MarriedSSC40M540002971.0000.0000.895-1.0000-1.00000.0000.00.0000.01.85200othersothers698101000.0000.01.0000.000101.0000.000100000001199
37639456451410.0Y57331101011010100000000000000010232001810181121136MarriedSSC29M18000661.0000.0000.9230.91310.92410.5560.00.5560.012.31200othersCC653202000.0000.01.0000.000100.5000.0000010001020148
28064342098610.0Y83-1-10-100000000000000000000050001011411SingleGRADUATE21M30000780.5000.0000.333-1.0000-1.00000.0000.00.0000.00.34600ConsumerLoanConsumerLoan666211000.0000.00.5000.500110.5000.5000002000020147
36194154293810.0Y42-1-10-10000-1-10075700000000001000000211100Single12TH22M10000471.0000.0000.836-1.0000-1.00000.0000.00.0000.0-1.00000othersothers682101000.0000.01.0000.000101.0000.000010000010077
34979452457811.0Y64-1-10-10000000000000000000006211200103110MarriedGRADUATE27M25000250.8890.5000.6050.62410.79910.0001.00.0000.57.69100CCConsumerLoan685918200.2220.00.8890.111410.4440.1112116001180232

Test

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18180827277310.0Y-1-1-10-10000-1-1002641000000000004000000221400SingleGRADUATE22F18000400.6001.01.000-1.0000-1.000.00.00.0000.0-1.00010othersothers708523200.4000.0000.6000.400200.4000.0003000500500452
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453316787910.0Y78-1-10-1000000000000000000000700000047633Married12TH35M15000261.0000.00.166-1.0000-1.000.00.00.0000.02.36700ConsumerLoanConsumerLoan655101000.0000.0001.0000.000101.0000.000000100001099
16618824931910.0Y993-1-10-100000000000000000000010000001259000MarriedSSC51M12500390.0000.00.000-1.0000-1.000.00.00.0000.0-1.00000ConsumerLoanConsumerLoan697110000.0000.0000.0001.000000.0000.00000010000104242
454576807310.0Y76-1-10-100000000000000000000011000212158310Married12TH38M250001300.6000.00.851-1.0000-1.000.50.00.5000.01.59300PLothers689523000.0000.0000.6000.400200.4000.0002001000324586
25611138425310.0Y292413433000000030000000000038300311180110Married12TH29M35000781.0000.00.3780.5381-1.001.00.00.3330.03.19400PLothers676404000.0000.0001.0000.000100.2500.00001300001307111